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1mo ago

Senior Clinical Informatics Data Scientist

University of Texas at Arlington Arlington, TX

Arlington, TX

1mo ago Closed

$92k - $130k/yr Full time Senior Clinical Informatics Clinical Informatics Data

This position is no longer accepting applications (closed Oct 18, 2026).

Job Description

The University of Texas at Arlington is seeking a Senior Clinical Informatics Data Scientist to strengthen its healthcare informatics research and education programs in collaboration with Cook Children’s Health Care System. In this onsite role in Arlington, TX, you will turn clinical and real-world data into research, education, and evidence-based insights while supporting reproducible, compliant study processes.

You will help build scalable capabilities for clinical data science, collaborate with clinicians and researchers on study design and analytical plans, and produce research outputs for academic, clinical, and operational stakeholders.

Responsibilities

Support UTA’s healthcare informatics education and research program in collaboration with Cook Children’s Health Care System, including clinical research, real-world evidence, and data-driven education initiatives.
Develop and maintain scalable infrastructure, tools, workflows, cohorts, data specifications, and reproducible research processes for clinical and real-world healthcare data projects.
Perform analyses using clinical and real-world healthcare data to generate insights, reports, publication-ready outputs, and findings.
Partner with clinicians, researchers, and technical teams to translate clinical and research questions into structured study designs, analytical plans, and data approaches.
Lead or contribute to manuscripts, abstracts, reports, and presentations for academic, clinical, and operational audiences.
Provide training, mentorship, and guidance to faculty, students, and collaborators on clinical data science, research methods, study design, and analytical best practices.
Apply clinical informatics, epidemiologic, cohort development, phenotype definition, data validation, governance, privacy, security, and regulatory methods to support research quality, validity, reproducibility, and compliance for sensitive clinical data.
Perform other duties as assigned.

Requirements

Doctoral degree or clinical doctorate with research training (examples include PhD, DrPH, MD, DO, PharmD, DNP, or equivalent), or master’s-level training with substantial applied healthcare data research experience.
Three (3) years of experience conducting healthcare data research, including experience with statistical analysis, study design, and working with clinical or real-world healthcare data.

Technologies

SQL, R, Python, SAS, Epic systems, Cosmos

Preferred Qualifications

Doctoral or clinical doctorate (examples include PhD, MD, DrPH, or equivalent) with experience using Epic systems, Cosmos, or similar healthcare data platforms.
Experience in clinical informatics, epidemiology, or real-world evidence research, along with scientific writing and publication experience.

Knowledge, Skills & Abilities

Proficiency in at least one analytic tool such as SQL, R, Python, SAS, or similar.
Strong understanding of clinical data, study design, and statistical methods.
Ability to translate clinical questions into analytical approaches.
Strong data analysis and problem-solving skills.
Effective communication with both technical and non-technical audiences.
Collaborative approach across teams and disciplines.

Work Schedule and Working Conditions

Work schedule: Monday through Friday; 8:00am through 5:00pm (in-office).
Working conditions: Designated essential personnel; may work off-site for critical operations.
Security-sensitive status: Position may be designated security-sensitive based on job duties or location.

Benefits

Benefit eligible: Yes
Comprehensive benefits package: https://www.uta.edu/hr/employee-benefits

Documents Needed to Apply

Resume (required)
Cover letter (required)
References (minimum of three) (required)

Additional Information

Preemployment background check: The University of Texas at Arlington conducts a pre employment background check on applicants under final consideration.
Critical infrastructure: If this position provides access to critical infrastructure as defined by Section 113.001(2) of the Texas Business and Commerce Code, continued employment requires maintaining security or integrity of that infrastructure per Texas Executive Order GA 48. Applicants authorize routine background checks if they have access to critical infrastructure.
ADA accommodations: For reasonable accommodation during the application/interview process, contact 817-272-5554 or email ADADocs@uta.edu.

Salary: USD 92,100 - 130,000 per year.

Experience: Minimum 3 years.

Location: Arlington, TX (onsite).

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1mo ago

Lead Data Engineer - Data Scientist

Wells Fargo Columbus, OH

Columbus, OH

1mo ago Closed

$119k - $206k/yr Full time Backend Developer Manager Analytics

This position is no longer accepting applications (closed Oct 18, 2026).

Job Description

Lead data engineering efforts in Cybersecurity within Identity Access Management, building data science and ML-powered solutions for access governance and anomaly detection.

Responsibilities

Lead complex initiatives with broad impact and participate in large-scale software planning for Identity and Access Management (IAM).
Design, develop, and run tooling to discover issues in data and applications, then report findings to engineering and product leadership.
Apply statistical and data science methods to IAM business problems.
Act as a subject matter expert on ML, AI, and mathematical and statistical techniques applied to large datasets.
Design, support, and operate data pipelines, data models, dashboards, and API integrations for real-time and batch analytics use cases.
Design and conduct experiments, statistical analyses, and hypothesis testing to evaluate proposals supporting controls, policies, and operational processes.
Build AI-powered capabilities to identify inappropriate access, recommend entitlements before users request them, and detect anomalous behavior across millions of identity events.
Lead IAM team members across operations, onboarding, initiatives, and engineering, plus line of business and lines of defense teams, to translate analytical needs into technical solutions.
Establish and implement engineering and analytical best practices for solution development.
Develop solutions in alignment with security, privacy, model risk, and regulatory guidelines.
Develop, test, deploy, and support ML-enabled analytical solutions, and guide teammates on ML, AI, and statistical techniques to drive AI/ML adoption across IAM.
Monitor model health, reliability, and drift and help ensure continuous improvement and required remediation.
Use AI-assisted development and analysis tools (including GitHub Copilot and approved code-centric agents) to accelerate system design, coding, testing, analysis, and troubleshooting.
Validate and integrate AI-assisted outputs with strong technical judgment, accounting for model limitations, security risks, and operational considerations.
Apply AI responsibly in development and production, ensuring alignment with security, compliance, privacy, and ethical standards.

Requirements

5+ years of Database Engineering experience, or equivalent demonstrated through one or a combination of: work experience, training, military experience, or education.
5+ years of experience with Python data science libraries including Pandas, NumPy, and Scikit-Learn, plus exposure to a deep learning framework such as TensorFlow or PyTorch.

Technologies

Python, Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch
Vertex AI, GCP, BigQuery
Neo4j
GitHub, Power BI, Tableau, Alteryx
GitHub Copilot, Claude Code, Cursor, Devin, LLMs, RAG, AI agents

Desired Qualifications

Knowledge of Vertex AI and GCP environments, including BigQuery and getting models into production on these platforms.
Knowledge of graph networks for anomaly detection using Neo4j.
A learning mindset to keep up to date with developments in the field.
Knowledge of GitHub for code management and familiarity with Power BI, Tableau, and Alteryx.
Understanding of software engineering fundamentals including testing, debugging, and code reviews is highly desirable.
Knowledge of mathematical foundations of statistics, machine learning, and modern AI techniques.
Strong understanding of model evaluation techniques for classification and clustering, including precision, accuracy, F1-scores, confusion matrices, and ROC curves.
High comfort using AI coding agents such as GitHub Copilot, Claude Code, Cursor, Devin, or others, with the ability to critically examine AI code for fitness for use.
Familiarity with LLMs, RAG, and AI agents.
Strong SQL skills and ability to work with relational and analytical databases.
Strong book-of-work management and organizational skills.
Hands-on ability to manipulate data and tools to prototype and present solutions.
Confident and self-motivated producer of original ideas and solutions, with sound judgment for when to escalate issues.
Strong written and verbal communicator with excellent presentation skills, including ability to communicate complex technical concepts.
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Engineering, or a related field.

Benefits

Health benefits
401(k) Plan
Paid time off
Disability benefits
Life insurance, critical illness insurance, and accident insurance
Parental leave
Critical caregiving leave
Discounts and savings
Commuter benefits
Tuition reimbursement
Scholarships for dependent children
Adoption reimbursement

Location and Work Mode

Columbus, Ohio (onsite)
3075 Loyalty Cir. - Columbus, Ohio 43219

Compensation

Salary range: $119,000 - $206,000 per year (range determined by location)
May be considered for a discretionary bonus, Restricted Share Rights, or other long-term incentive awards

Posting Statements

Job posting may come down early due to volume of applicants.
Required location(s) listed above; relocation assistance is not available.
Not eligible for visa sponsorship.
Role is not eligible for 100% remote work.
Posting end date: 22 Sep 2026.

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1mo ago

Sr. Data Scientist, Technology Development Data Analytics

Cabaletta Bio Philadelphia, PA

Philadelphia, PA

1mo ago Closed

Full time Senior Data Analysis Data Analytics

This position is no longer accepting applications (closed Oct 18, 2026).

Job Description

Build data science and data-driven capabilities across CMC by combining scientific, statistical, and computational methods to support development and manufacturing decisions.

Responsibilities

Partner with CMC subject matter experts to define scientific and technical questions addressable through data science, data integration, visualization, statistical analysis, and modeling.
Integrate and analyze data spanning process development, analytical development, manufacturing, raw materials, and product quality to strengthen process and product understanding.
Create visualizations, dashboards, and analytical workflows that help scientists and technical leaders explore data, detect trends, and make informed decisions.
Establish scalable, reproducible methods for organizing, integrating, analyzing, and visualizing CMC data from internal and external sources.
Use and expand expertise in statistical and computational tools including JMP, Python, R, SQL, Spotfire, Power BI, or similar platforms.
Identify and evaluate opportunities for advanced analytics, automation, machine learning, and emerging AI-enabled approaches for relevant CMC scientific and operational challenges.
Independently lead data-focused projects from problem definition and data assessment through analysis, interpretation, visualization, and conclusions.
Structure scientific questions and analytical paths when working with complex and imperfect datasets.
Communicate analytical approaches, assumptions, limitations, and conclusions clearly to technical and non-technical stakeholders.
Develop reusable analytical workflows, tools, and best practices that improve efficiency, consistency, and data accessibility across Technical Development.
Maintain scientific integrity by seeking and incorporating feedback and collaborating across functions in a culture of scientific curiosity, continuous learning, accountability, and open communication.
Perform other related duties as assigned.

Requirements

Ph.D. or M.S. in Data Science, Statistics, Computer Science, Bioinformatics, or another relevant scientific, engineering, computational, or quantitative discipline.
Demonstrated experience using data to solve complex scientific or technical problems.
Strong scientific and quantitative problem-solving skills, including the ability to translate complex or ambiguous questions into structured analytical approaches.
Experience with statistical analysis, experimental design, data visualization, and/or analysis of complex scientific datasets.
Experience with Design of Experiments (DoE), continued process verification (CPV), or statistical process control (SPC) is a plus.
Experience with, or ability and motivation to learn, analytical and computational tools such as JMP, Python, R, SQL, Spotfire, Power BI, Tableau, or equivalent platforms.
Highly organized with a demonstrated ability to manage multiple analyses, datasets, priorities, and projects independently.
Strong interpersonal, written, and verbal communication skills for cross-disciplinary collaboration.
Demonstrated initiative and ownership, with motivation to build new capabilities and solve problems beyond established approaches.
Preferred: Experience in biotechnology, pharmaceutical development, biologics, cell and gene therapy, GxP/regulated manufacturing environments, or other advanced therapeutic modalities.

Technologies

JMP
Python
R
SQL
Spotfire
Power BI
Tableau

Benefits

Health and retirement
PTO
Stock option plans

About the Position

Apply scientific, statistical, and computational approaches to help build and advance data science and data-driven capabilities across CMC.
Work at the intersection of CMC science and data analytics to integrate and analyze data, identify trends and relationships, and translate complex datasets into actionable scientific insights.
Partner with Process Development, Analytical Development, Manufacturing, Quality, Supply Chain, and other cross-functional teams.
Bring strengths in scientific curiosity, learning agility, initiative, integrity, organization, communication, and motivation to develop new quantitative and computational capabilities.
Enjoy open-ended problem-solving, working with ambiguity, and building new capabilities within Technical Development.

Required Qualifications

2+ years of relevant industry experience with a Ph.D. or 5+ years with an M.S. is preferred; candidates with equivalent demonstrated experience are encouraged to apply.

Location: Philadelphia, PA (onsite)

Summary: Sr. Data Scientist, Technical Development Data Analytics will build and advance data science and data-driven capabilities across CMC by applying scientific, statistical, and computational approaches to development and manufacturing challenges.

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1mo ago

Data Scientist II - ML Engineering

HEB San Antonio, TX

San Antonio, TX

1mo ago Closed

Full time Artificial Intelligence CI/CD Data Pipeline

This position is no longer accepting applications (closed Oct 18, 2026).

Job Description

This role is focused on ML engineering, including the build and maintenance of an ML platform and the production model lifecycle. You will also contribute to Generative AI engineering, with emphasis on RAG, search, knowledge graphs, and multi-agent workflows.

What You’ll Do

Design, create, and maintain an ML platform and related environments.
Manage Docker containers and Kubernetes clusters, including dependencies and configurations.
Implement CI/CD pipelines to automate building, testing, and deployment of machine learning models.
Monitor and optimize model training performance and resource usage.
Deploy ML models to production, with model versioning and rollback mechanisms.
Enable scalable, reliable model serving using tools such as Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI, to support increasing model complexity and volume.
Collaborate with data scientists, data engineers, and stakeholders to understand and meet infrastructure needs.
Stay current with the latest technologies and best practices in ML infrastructure.
Architect and develop Generative AI solutions using Machine Learning and GenAI techniques.
Specialize in engineering and deploying Generative AI models, with focus on Retrieval-Augmented Generation (RAG) systems, search, knowledge graphs, and multi-agent workflows.
Handle both unstructured and structured data to prepare context for Language Model Learning (LLM), including embedding large text corpora, developing generative SQL queries, and building connectors to structured databases.
Train models on prepared data and optimize fine-tuning hyperparameters for strong performance.
Build a framework to stitch cross-domain learning and optimize toward mission-specific and multi-mission tasks.
Serve as an expert in AI interpretation and causality by identifying model causality relationships and building a framework to measure bias, underspecification, and latent drivers with their connections.
Create an enterprise domain-specific reasoning system to improve actionable insights and optimize resources used across the machine learning process.
Orchestrate a reusable storytelling methodology to support AI translation, applying inquisitive, transparency-focused approaches to business stakeholders.
Apply AI reasoning into business action recommendations and use AI research to accelerate business innovation.

Required Qualifications

A related degree or comparable formal training, certification, or work experience.
5+ years of experience in a retail or retail-related decision science role.
Expertise in ML visualization flow.
Expertise in optimizing distributed machine learning in a heterogeneous domain environment.
Technical knowledge in programming languages: SQL, R, Python, Scala, Java, C/C++.
Technical knowledge in big data and ML optimization: GPU code optimization, Horovod, Spark MLlib optimization, Cython, JNI, Numba.
Technical knowledge in mainstream ML/AI: manifold learning, distributed clustering, graph network, hierarchical model, Bayesian network, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-Learning, federated learning.
Technical skills to apply causal reasoning representation and learning, including human-centric, explainable, responsible AI.
Ability to work comfortably with imperfect or incomplete data.
Ability to apply AI reasoning into business action recommendation.
Ability to work in a fast-paced retail environment with frequently shifting priorities.
Work extended hours; sit for long periods.

Core Technologies

Docker, Kubernetes, CI/CD
Vertex, Databricks, TensorFlow Serving, Flask, FastAPI
Machine Learning, GenAI, Retrieval-Augmented Generation (RAG), Language Model Learning (LLM)
SQL, R, Python, Scala, Java, C/C++, GPU code optimization
Horovod, Spark MLlib, Cython, JNI, Numba
Manifold learning, distributed clustering, graph network, hierarchical model, Bayesian network, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-Learning, federated learning
Generative AI

Location

San Antonio, TX (onsite)

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1mo ago

Data Scientist II - RiskOS

Socure Miami, FL

Miami, FL

1mo ago Closed

$140k - $170k/yr Full time Artificial Intelligence Big Data Data Integration

This position is no longer accepting applications (closed Oct 18, 2026).

Job Description

This role supports Socure’s RiskOS Workforce Verification vertical by delivering an end-to-end data science pipeline to identify hiring and workforce identity fraud. The position also includes GenAI and NLP components for resume verification and explanation over unstructured text.

Key Responsibilities

Own the full data science lifecycle for Workforce Verification use cases on RiskOS, covering data exploration and hypothesis generation through model development, evaluation, deployment, and monitoring.
Explore and analyze workforce-related data sources including applications, resumes, device and behavioral telemetry, background checks, and ATS/HRIS integrations to detect patterns of workforce fraud.
Design, implement, and iterate on rules, conditions, and heuristic logic in RiskOS workflows to identify high-risk workforce events, such as repeated identities across multiple resumes, suspicious device patterns, and anomalous hiring flows.
Develop and evaluate machine learning models for workforce risk and identity assessment, including fraud risk scoring, clustering related identities, and anomaly detection across hiring funnels, using Socure’s broader identity and device signals where applicable.
Collaborate with RiskOS and Workforce product teams on GenAI-powered features like the Resume Verification Agent and explanation agents by defining input/output requirements, building evaluation datasets, and establishing quantitative and qualitative evaluation frameworks for LLM components.
Work closely with engineering to productionize models, rulesets, and GenAI components within RiskOS by defining interfaces, supporting integration and testing, and contributing to monitoring, alerting, and feedback loops.
Translate model and rule performance into clear customer-facing narratives with product, Workforce GTM, and solution consulting, including outcomes such as blocking fake applicants and reducing deepfake interviews or identity rental in hiring.
Incorporate customer feedback and outcome data to continuously improve Workforce Verification logic and models, and support experimentation and offline “test harness” design for safe evaluation of new workflows and templates.
Operate with a product mindset by documenting assumptions, decisions, and evaluation results, communicating trade-offs clearly, and proactively surfacing risks, limitations, and opportunities.

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience.
3–6 years of hands-on experience in data science, machine learning, or applied analytics, with meaningful experience in fraud, risk, trust & safety, or workforce or hiring analytics preferred.
Experience owning end-to-end analytics and/or model development projects, including problem framing, data wrangling, feature engineering, model training, evaluation, and deployment support.
Strong proficiency in Python and SQL, including experience with common data science and ML libraries such as pandas, scikit-learn, XGBoost, PySpark, or similar.
Comfort working with large, messy, heterogeneous datasets including JSON workflows, logs, event streams, and third-party enrichments, plus the ability to create reusable abstractions or utilities.
Exposure to Natural Language Processing and/or unstructured text analytics such as resume or document parsing, entity extraction, similarity search, or basic embedding-based methods applied in real-world products.
Some hands-on experience with Generative AI or LLM-based products (for example, commercial LLM APIs, prompt design, RAG-style retrieval, or evaluation of LLM outputs), with interest in further developing these skills.
Strong analytical and problem-solving skills, including reasoning about ambiguous signals and adversarial behavior in fraud or workforce contexts.
Ability and willingness to perform light data engineering or production-oriented tasks as needed in collaboration with engineering, such as building ETL transforms, contributing to Airflow/Spark jobs, or instrumenting basic monitoring.
Clear, concise communication skills for explaining complex analyses, models, and GenAI behavior to non-technical stakeholders such as product, GTM, and customers.
A bias toward ownership, learning, and collaboration in a fast-paced, evolving environment, with comfort receiving guidance from senior data scientists while expanding scope and autonomy.

Technologies

Python, SQL, pandas, scikit-learn, XGBoost, PySpark, Natural Language Processing, GenAI, LLMs, RAG-style retrieval, ETL, Airflow, Spark

Nice to Have

Direct experience with workforce, HR tech, ATS/HRIS data, or hiring funnel analytics.
Prior work on identity verification, device intelligence, or orchestration and rules engines (such as RiskOS or similar systems).
Familiarity with evaluation and monitoring of GenAI systems, including offline benchmarks, human-in-the-loop review, and safety or hallucination checks.

Role Details

Location: Miami, FL (onsite)
Experience: 3+ years
Compensation: USD 140,000 - 170,000 per year

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1mo ago

Data Scientist

Caterpillar East Peoria, IL

East Peoria, IL

1mo ago Closed

$98k - $158k/yr Full time Analytics Artificial Intelligence Data Analysis

This position is no longer accepting applications (closed Oct 18, 2026).

Job Description

Caterpillar is seeking a Data Scientist to support its Power and Energy Drivetrain team through analysis of large volumes of data and development of practical, data-driven solutions. This onsite role focuses on building and refining data models, applying Natural Language Processing and machine learning, and communicating findings to management through data visualization.

What you’ll do

Lead data gathering, data mining, and data processing for very large volumes, creating data models that fit the analysis goals.
Explore, promote, and implement semantic data capabilities using Natural Language Processing, text analysis, and machine learning techniques.
Help define analysis requirements and scope, then present business insights to management using data visualization technologies.
Conduct research to optimize data models and algorithms to improve effectiveness and accuracy.
Design and develop solutions by collaborating with senior stakeholders and business users, gathering VOB/VOC and business requirements, and preparing implementation plans for deployment.
Contribute to strategic initiatives, including development of standardized best practices, offerings, services, and associated tools and training.

What you bring

Knowledge of statistical tools, processes, and practices to describe business results in measurable terms and support business decisions.
Ability to explain the basic decision process tied to specific statistics.
Capability to work with basic statistical functions on a spreadsheet or calculator.
Understanding of common statistical errors, misinterpretations, and misrepresentations, including why they occur.
Familiarity with sample size characteristics, normal distributions, and standard deviation; ability to generate and interpret basic statistical data.
Commitment to accuracy, with the ability to complete tasks with high precision.
Strong attention to detail when processing large quantities of information and an ability to balance speed and accuracy.
Experience evaluating and contributing to best practices, including cross-checking approaches and quality assurance tools, techniques, and standards.
Ability to use techniques that support effective analysis, identify root causes of organizational problems, and propose alternative solutions.
Comfort defining problems and assessing significance, and comparing two or more alternative solutions systematically.
Skill using visual/analytical methods such as flow charts, Pareto charts, and fish diagrams to identify meaningful data patterns.
Ability to apply logic and intuition to interpret data meaning and draw conclusions.
Knowledge of machine learning principles, technologies, and algorithms, with ability to develop, implement, and deliver related systems, products, and services.
Experience completing tasks and initiatives using machine learning technologies, including search engine optimization.
Use of tools and techniques for descriptive and inferential statistics.
Hands-on use of programming languages and tools for machine learning, including R and Python.
Ability to perform data mining and cleaning activities.
Capacity to diagnose and report minor or routine issues related to programming languages.
Knowledge of data management systems, including using and supporting access for searching, extracting, and formatting data.
Ability to define, create, and test simple queries using command language in a specific environment.
Ability to apply query tools to connect to a data warehouse, analyze query access path information and results, and use tested query statements to retrieve, insert, update, and delete information.
Working knowledge of advanced query features such as sorting, filtering, and making simple calculations.
Knowledge of business process improvement best practices, including methods to identify, evaluate, introduce, and implement more efficient approaches.
Ability to move beyond reporting by translating data into recommendations that drive business decisions and process improvements.
Experience working with enterprise data platforms such as Snowflake, including querying, validating, and connecting data sources to reporting and visualization tools.

Technologies you’ll use

Natural Language Processing
Machine learning
Search engine optimization
R
Python
Snowflake

Location and schedule

East Peoria, IL (onsite)
Onsite requirement: five days a week

Compensation

Pay range: USD 97,530 - 158,480 per year.

Benefits

Medical, dental, and vision benefits
Paid time off plan (Vacation, Holidays, Volunteer, etc.)
401(k) savings plans
Health Savings Account (HSA)
Flexible Spending Accounts (FSAs)
Health Lifestyle Programs
Employee Assistance Program
Voluntary Benefits and Employee Discounts
Career Development
Incentive bonus
Disability benefits
Life Insurance
Parental leave
Adoption benefits
Tuition Reimbursement
These benefits also apply to part-time employees

Additional information

Relocation is not available for this position.
Sponsorship is not offered for this position.
Visa Sponsorship is not available for this position.

Posting dates: September 17, 2026 - September 24, 2026

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1mo ago

Data Scientist I

Caterpillar Chicago, IL

Chicago, IL

1mo ago Closed

$89k - $145k/yr Full time Python Analytics Artificial Intelligence

This position is no longer accepting applications (closed Oct 18, 2026).

Job Description

Join Caterpillar’s Cat Digital organization as a Data Scientist I supporting the Equipment Usage Analytics team. The role focuses on building analytics to predict and measure application usage, downtime, and efficiency for a large fleet of connected assets using internal and external data.

Role Summary

You will develop analytics workflows and models that gather, mine, process, and analyze large-scale data volumes. The work includes applying Natural Language Processing, text analysis, and machine learning to produce business insights related to equipment usage and performance.

Key Responsibilities

Perform data gathering, data mining, and data processing at large scale, including creating appropriate data models.
Explore, promote, and implement semantic data capabilities using Natural Language Processing, text analysis, and machine learning techniques.
Define requirements and scope for data analyses, and present/report potential business insights to management using data visualization technologies.
Conduct research to improve data model optimization and algorithms for greater effectiveness and accuracy in analyses.

Required Qualifications

Working knowledge of statistical tools, processes, and practices to express business results in measurable scales, supporting business decision-making.
Working knowledge of machine learning principles, technologies, and algorithms, with the ability to develop, implement, and deliver related systems, products, and services.
Working knowledge of applying Python to solve business challenges, including using tools, techniques, and platforms to write and modify programming.
Working knowledge of data management systems, including the ability to use, support, and access facilities for searching, extracting, and formatting data for further use.
Typically holds a Bachelors, Masters, or PhD in Applied Statistics, Data Science, Business Analytics, Predictive Analytics, Business Intelligence & Analytics, Mathematics, Computer Science, Engineering (including Aerospace, Electrical, Mechanical, Computer, Industrial, Agricultural), or an equivalent technical degree.
Experience applying Python (including NumPy, SciPy, pandas, etc.) to solve business challenges.
Experience with advanced data analysis and statistical methods such as regression, hypothesis testing, ANOVA, statistical process control, etc. (typically 1+ years).
Working knowledge of cloud technologies such as AWS, Azure, or Google Cloud.
Advanced experience with version control and repositories such as GitHub.
Must demonstrate initiative, interpersonal skills, and the ability to communicate effectively.