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2mo ago

Data Scientist

Leidos Maryland 🇺🇸 Remote

Maryland

2mo ago Closed

$70k - $126k/yr Full time Python Analytics Artificial Intelligence

This position is no longer accepting applications (closed Aug 23, 2026).

Job Description

Data Scientist at Leidos focused on mission-focused big data and predictive analytics for national security, with research-level analytics and cross-functional collaboration, and flexible on-site/remote work based on client requirements.

Responsibilities

Collaborate with a cross-disciplinary team of data scientists, software engineers, network specialists, senior investigators, program managers, researchers, and data analysts to design, build, and refine a Search and Discovery platform that locates, organizes results, disseminates findings via reports, and supports publications and presentations.
Pursue research tasks to identify optimal indexing and data organization strategies to enhance discovery.
Develop agents and tools as reusable elements within a broader agent-based solution space.
Manage multiple concurrent projects, use open-ended guidance, and independently or with teammates uncover mission-relevant insights, then package and present findings to non-technical audiences.
Apply curiosity, quantitative acumen, and customer focus to identify novel data sources across fields, improve predictive algorithm performance, and promote adoption of advanced data analytics platforms with a skilled, motivated team.
Utilize expertise in research design, exploratory analysis, quantitative methods, UI application design, and customer outreach and engagement.

Requirements

Bachelor's degree in a quantitative or analytical field (examples include Computer Science, Mathematics, Economics, Statistics, Engineering, Physics, or Computational Social Science) or Master's degree or equivalent graduate degree with certificate-based advanced training.
4+ years of experience in data science, analytics, or quantitative intelligence analysis, demonstrating progressive technical development; with a Master's degree, 2+ years of experience is required.
Active TS/SCI clearance required; able to obtain and maintain a polygraph.
Proficient in Python.
Experience deploying data science applications (for example Streamlit) using Docker, Kubernetes, and/or OpenShift.
Experience with information retrieval and search technologies, such as Elastic Stack (Elasticsearch, Kibana) and SQL.
Familiar with Git and cooperative software development practices.
Experience working with a hybrid team of analysts, engineers, and developers to research, build, and deploy complex yet user-friendly algorithms and analytics platforms.
Demonstrated active learning and creative problem solving.
Experience in co-development and cooperative research to maximize reuse of team products.

Technologies

Python
Docker
Kubernetes
OpenShift
Elastic Stack (Elasticsearch, Kibana)
SQL
Git
Streamlit
Agno
CrewAI
Pydantic AI
LangGraph

Benefits

Competitive compensation
Health and Wellness programs
Income Protection
Paid Leave
Retirement

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2mo ago

Data Scientist II, Anti Money Laundering Transaction Monitoring

BMO Financial Group Chicago, IL

Chicago, IL

2mo ago Closed

$69k - $128k/yr Full time Ai Ml AML Aml Ctr

This position is no longer accepting applications (closed Aug 23, 2026).

Job Description

BMO Financial Group is seeking a Data Scientist II to advance its AML transaction monitoring program from the Chicago area. The role centers on the design, development, and deployment of machine learning and analytical models for detecting money laundering activity and supporting the model lifecycle, with collaboration across AML Compliance, investigations, governance, and technology teams.

Responsibilities

Design, build, test, and deploy machine learning and analytical models used for AML transaction monitoring and suspicious activity detection
Analyze large volumes of customer, transactional, payment, and alert data to identify emerging money laundering risks, typologies, and anomalous behavior patterns
Develop and evaluate supervised and unsupervised machine learning approaches to improve detection effectiveness and investigator outcomes
Conduct feature engineering and exploratory data analysis to identify behavioral indicators associated with financial crime risk
Perform quantitative assessments of model performance, including detection effectiveness, productivity, false positive reduction, and risk coverage
Support model tuning, optimization, and revalidation activities to ensure models continue to perform as intended
Assess data quality and data lineage and partner with data management teams to resolve issues affecting model performance and reliability
Prepare clear and comprehensive model development, testing, and governance documentation to support model validation, audit, and regulatory reviews
Translate analytical findings into actionable recommendations for AML Compliance, FIU, and senior management
Collaborate with business stakeholders, investigators, model governance, and technology partners to prioritize enhancements and implement solutions
Research emerging financial crime typologies, machine learning techniques, and industry best practices to continuously improve monitoring capabilities
Contribute to strategic initiatives involving AI, machine learning, graph analytics, network analysis, and other advanced analytical approaches applicable to financial crime detection
Ensure all analysis and model development activities comply with regulatory requirements, internal policies, and model risk management standards
Take measured risks while protecting the bank by applying the Risk Management Framework and exercising sound risk-based judgment

Requirements

Typically between 4 - 6 years of relevant experience and a post-secondary degree in a related field or an equivalent combination of education and experience
Experience with Python and SQL for large-scale data analysis, feature engineering, and model development
Strong understanding of statistical analysis, machine learning algorithms, and model performance measurement
Experience working with large structured and semi-structured datasets
Experience using version control tools and code repositories (e.g., Git, GitHub, Azure DevOps) to support collaborative development and reproducible analytical workflows
Familiarity with generative AI tools and AI-assisted development practices (e.g., Microsoft 365 Copilot, GitHub Copilot, LLM-based coding assistants) to improve productivity, documentation, and analytical workflows
Ability to communicate technical concepts effectively to both technical and non-technical audiences

Technologies

Python
SQL
Git
GitHub
Azure DevOps
Dataiku
Databricks
SAS
Spark
Hadoop
Microsoft 365 Copilot
GitHub Copilot
LLM-based coding assistants
Cloud-based analytics platforms

Benefits

Performance-based incentives
Discretionary bonuses
Health insurance
Tuition reimbursement
Accident and life insurance
Retirement savings plans

Application deadline

08/04/2026

Location

Chicago, IL (onsite)

Address

320 S Canal Street

Salary

USD 69,000 - 127,800 per year — Salaried

Job Family Group

Data Analytics & Reporting

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2mo ago

Analytics Product Owner

InterContinental Hotels Group Atlanta, GA

Atlanta, GA

2mo ago Closed

$106k - $125k/yr Full time Ai Products Analytics Business Analytics

This position is no longer accepting applications (closed Aug 23, 2026).

Job Description

InterContinental Hotels Group is expanding its AI and BI Center of Enablement with an Analytics Product Owner who will shape the vision and roadmap for data, analytics, and AI initiatives. Based in Atlanta with a hybrid work arrangement, this role translates business goals into actionable backlogs and guides the evolution of enterprise tools such as the Self-Service Toolkit, Tableau Cloud, the Enterprise Reporting Portal, and Conversational Data Intelligence. A bachelor’s degree (or equivalent) in a relevant field is required, and the position offers a salary range of USD 106,000 to 125,000 per year.

Responsibilities

Own the product vision and roadmap by identifying gaps between current data and AI capabilities and business outcomes, documenting requirements for cross-functional projects across the C4E portfolio.
Serve as the strategic owner for enterprise visualization platforms, with a focus on Tableau Cloud, and establish governance, site architecture, security models, user enablement, and scalable performance.
Analyze data sources, metadata frameworks, and user needs to design solutions spanning reporting portals, conversational data interfaces, and self-service BI tools.
Maintain deep technical expertise of enablement products (Self-Service Toolkit, Enterprise Reporting Portal, Conversational Data Intelligence) while balancing knowledge of Google Cloud Platform services, BigQuery, and Dataplex.
Maintain an architectural understanding of data products and metadata management, particularly at interfaces with internal systems and IHG business processes.
Collaborate directly with business partners as a trusted advisor, enabling them to implement BI initiatives and AI/ML experiments within governed environments.
Translate complex data, BI, and AI concepts into a compelling business narrative for stakeholders, and convert business requirements into clear technical specifications for engineering and enablement teams.
Create and validate data quality test scripts, BI platform performance metrics, visualization accuracy scenarios, and user acceptance testing for the C4E product portfolio.
Create and prioritize features and user stories, conveying the vision and requirements for reporting portals, conversational data interfaces, platform governance, and AI enablement products to the development team.

Requirements

Advanced knowledge of product ownership within data, BI, or AI contexts, with a solid understanding of modern data architecture, systems, and enablement frameworks.
Strong proficiency with Tableau, Google Cloud Platform (GCP), and BigQuery, including experience managing, governing, and scaling Tableau Cloud as an enterprise platform, security administration, content lifecycle management, and platform architecture.
Expertise in SQL for complex data analysis and a solid understanding of data modeling, data warehousing, and metadata management/cataloging (Dataplex/Knowledge Catalog).
Familiarity with or a strong interest in AI/ML initiatives, including Conversational Data Intelligence capabilities, Vertex AI, and Gemini Enterprise.
Ability to understand highly complex data and technical problems and build scalable, cross-functional solutions that promote self-service capabilities.
Exceptional storytelling and communication skills, with the ability to translate technical information into business-friendly language and distill business needs into technical requirements.
Collaborative leadership to work with Data Engineers, Cloud Architects, and Platform Admins on large, cross-functional projects.
Experience in Agile or Scaled Agile Framework (SAFe) environments.
Experience working with cloud-based data platforms.
Bachelor’s degree (or equivalent) in a relevant field.

Technologies

Tableau Cloud
Google Cloud Platform (GCP)
BigQuery
Dataplex (Knowledge Catalog)
Self-Service Toolkit
Enterprise Reporting Portal
Conversational Data Intelligence
Vertex AI
Gemini Enterprise
SQL

Benefits

Paid time off
Medical/dental/vision insurance
401K
Bonus pay

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2mo ago

Senior AI Engineer

Diverse Lynx Aguadilla

Aguadilla

2mo ago Closed

$50 - $55/hr Contract Senior Agentic Ai AI

This position is no longer accepting applications (closed Aug 23, 2026).

Job Description

Diverse Lynx is seeking a Senior AI Engineer to design, develop, and deploy Agentic AI solutions with a focus on autonomous multi-agent systems. This onsite contract role in Aguadilla, PR involves collaborating with business, data, and platform teams to deliver scalable, secure, production-grade Agentic AI platforms.

Location: Aguadilla, PR (onsite)

Job type: Contract

Salary: USD 50 - 55 per hourly

Responsibilities

Design and build Agentic AI systems including planning, reasoning, task execution, memory, and feedback loops
Develop multi agent architectures (task agents, reasoning agents, tool calling agents, orchestration agents)
Implement RAG, Knowledge Graph, and Memory based AI patterns for enterprise use cases
Integrate LLMs with enterprise systems, APIs, tools, and workflows
Define end to end AI solution architecture from data ingestion to inference and observability
Build reusable AI services, agent frameworks, and orchestration layers
Ensure scalability, resilience, performance, and security of AI solutions
Collaborate with cloud, data, and MLOps teams to productionize solutions
Define agent evaluation metrics (accuracy, autonomy, cost, latency, safety)
Embed Responsible AI guardrails including governance, explainability, auditability, and compliance
Work with product owners, domain SMEs, and architects to translate business problems into AI solutions
Mentor junior engineers and contribute to AI engineering standards and best practices
Support client discussions, solution walkthroughs, and technical decision making

Requirements

Strong proficiency in Python (mandatory)
Hands on experience with Agentic AI frameworks (e.g., LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI or equivalent)
Experience with LLMs (OpenAI, Azure OpenAI, Anthropic, open source models)
Expertise in RAG, embeddings, vector databases, and prompt engineering
Experience building multi agent orchestration, tool calling, and workflow automation
Experience with vector databases (Pinecone, FAISS, Weaviate, Chroma, etc.)
Knowledge of knowledge graphs, ontologies, and semantic search
Cloud experience on Azure / AWS / GCP
Familiarity with APIs, microservices, event driven architectures
Model monitoring, evaluation, and cost optimization
8 12+ years of overall software / AI engineering experience
3+ years hands on experience in GenAI and emerging Agentic AI systems
Experience delivering production grade AI solutions in enterprise environments
Bachelor s or Master s degree in Computer Science, AI, Data Science, or related field

Technologies

Python
LangGraph
LangChain
LlamaIndex
AutoGen
CrewAI
OpenAI
Azure OpenAI
Anthropic
Pinecone
FAISS
Weaviate
Chroma
Azure
AWS
GCP
APIs
microservices
event driven architectures
Knowledge Graph

NICE TO HAVE

Experience in regulated industries (pharma, healthcare, life sciences)
Exposure to knowledge graphs, scientific/R&D workflows, or enterprise automation

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2mo ago

Applied AI Engineer II

Deloitte Atlanta, GA

Atlanta, GA

2mo ago Closed

$103k - $189k/yr Full time AI Applied Engineer

This position is no longer accepting applications (closed Aug 23, 2026).

Job Description

At Deloitte, the on-site Applied AI Engineer II in Atlanta helps turn ideas into full‑stack products powered by GenAI and agentic capabilities. You’ll collaborate across product and AI investments to deliver outcomes that matter, guiding ideas from concept through production with a strong emphasis on quality and impact. This role offers a competitive annual salary range of USD 102,500 to 188,900, onsite work in Atlanta, and eligibility for a discretionary annual incentive program. You’ll join a team that values practical engineering, measurable results, and close collaboration with cross‑functional partners.

Responsibilities

Outcome driven accountability: develop engineering solutions that address complex problems with meaningful business value, maintaining lean designs and controlling inference, token, and cloud costs.
Technical leadership and advocacy: act as the technical champion for products, ensuring code integrity, feasibility, and alignment with business and customer goals; participate across requirements, design, development, testing, integrations, and support.
Engineering craftsmanship: uphold code and design integrity, align implementation with architecture and tech stack, ensure quality and maintainability; be hands‑on, continuously learn new approaches, and produce technical specifications and scalable, well‑documented code that meets quality KPIs; collaborate effectively with diverse teams.
Customer centric engineering: develop lean solutions through rapid, low‑cost experimentation to solve customer needs; engage with customers and product teams before, during, and after delivery to ensure timely and appropriate solutions.
Incremental and iterative delivery: prioritize action and evidence over extensive planning; navigate complexity with a forward‑leaning approach to deliver maintainable solutions.
Cross‑functional collaboration and integration: work with product management, design, and delivery teams; balance feasibility, viability, usability, and value by integrating diverse perspectives; foster a collaborative environment that enhances innovation.
Advanced technical proficiency: apply modern software engineering practices including AI and agentic SSDLC to deliver frequent product deployments with full automation from discovery through production and operations, supported by quality checks throughout the lifecycle; model and promote these techniques to improve solutioning and delivery; focus on continuous improvement and lifecycle understanding.
Domain expertise: quickly acquire domain knowledge relevant to the business or product and translate business needs, architectures, and UX/UI designs into technical specifications and code; contribute as a flexible, quality‑focused team member, mindful of tech debt payoff.
Effective communication and influence: articulate complex technical concepts clearly, persuading teammates and product teams with evidence and well‑structured trade‑offs; craft narratives that align technical solutions with business objectives.
Engagement and collaborative co‑creation: engage with product engineering teams at all levels and with customers as needed; cultivate constructive relationships and a culture of co‑creation to achieve product goals.

Requirements

Ability to work independently and as part of a team
Strong written and verbal communication skills
Meticulous attention to detail and quality of work
Ability to build and sustain professional relationships
Ability to lead projects or workstreams
Ability to manage and prioritize multiple tasks in a fast paced environment
Strong interpersonal skills and professional demeanor
Ability to meet deadlines and provide clear guidance to others
Bachelor's degree in computer science, software engineering, data science, machine learning, or related discipline
3+ years of experience with Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, and unit testing frameworks
2+ years building AI/ML and agentic applications with hands‑on GenAI experience across LLM integration (OpenAI, Anthropic, or open‑source models), RAG pipelines, prompt engineering, vector databases, evaluations, and AI agent orchestration
2+ years of cloud‑native engineering using FaaS, PaaS, or microservices on Azure, AWS, or GCP, including AI/ML services (Azure OpenAI, AWS Bedrock, Vertex AI) and infrastructure‑as‑code with FinOps accountability
Experience with business context diagrams, sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and AI‑augmented spec‑driven development
Familiarity with XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (eg LangFuse, LangSmith) for rapid, high‑quality product delivery
Ability to travel approximately 10% depending on project needs
Limited immigration sponsorship may be available

Technologies

Angular, React, NodeJS, Python, C#, .NET, Java
SQL/NoSQL, PyTorch, TensorFlow
LangChain, LangGraph
OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI
Azure, GCP, GitHub, ADO, SonarQube, MLflow
LangFuse, LangSmith

Benefits

Discretionary annual incentive program

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2mo ago

Senior Data Engineer

Stellantis Auburn Hills, MI

Auburn Hills, MI

2mo ago Closed

Full time Senior Big Data Bigdata

This position is no longer accepting applications (closed Aug 23, 2026).

Job Description

Senior Data Engineer at Stellantis, onsite in Auburn Hills, MI, focused on building reliable data foundations, pipelines and governance to scale AI delivery and industrialize data engineering practices within the Supply Chain AI Hub.

Responsibilities

Design, enhance or govern data models, transformation logic, and pipeline components that enable AI and analytics use cases
Foster maintainable architectures, reusable components, and clear lineage across transformations where applicable
Guide delivery teams with practical data engineering discipline rather than ad hoc builds
Clarify source to platform pathways, integration dependencies, and constraints impacting delivery
Improve visibility of traceability, handoffs, and access controls across the Supply Chain
Collaborate with ICT and engineering stakeholders to keep the build path practical and scalable
Contribute to quality checks, certification routines, governance expectations, or compliance-related traceability as scoped
Surface data structure issues, documentation gaps, or control weaknesses that affect deployment readiness
Support a trusted delivery environment by making data assets more visible, understandable, and maintainable

Requirements

Bachelor's or Master’s degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or related field
Eight years of experience in data engineering or data platforms
Prior experience in Supply Chain
Hands-on experience with modern data platforms such as Databricks, Spark, Snowflake, or equivalent
Experience with data pipelines, integration, semantics, lineage, architecture and platform environments
Enterprise-scale data transformation and delivery experience
Ability to collaborate effectively with analytics, AI, and software engineering teams

Technologies

Databricks
Spark
Snowflake
SQL

YOUR PROFILE

Strong data-engineering experience in modern enterprise environments, with depth in data modelling, pipelines, integration, quality, lineage or governance topics
Able to operate across business needs, technical constraints and delivery realities
Strong SQL and practical understanding of data structures, transformations, traceability and controlled delivery environments
Comfortable working with multiple stakeholders across architecture, data, engineering and governance topics
Structured, pragmatic and able to take ownership of a defined subset of a broader senior data-engineering scope

SKILLS YOU'LL GROW

Broader exposure across the building blocks that make AI-ready data scalable in operation
Experience at the intersection of data engineering, integration, quality and delivery governance
Opportunity to deepen expertise in a specific component while contributing to a wider AI data foundation program

WHY JOIN / IMPACT

Work on data-engineering challenges directly tied to real AI deployment in Supply Chain
Role with enough breadth to offer variety while maintaining focused ownership within a defined perimeter
Help strengthen the data foundations that enable scalable AI delivery

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2mo ago

Data Engineer Staff

Lockheed Martin Corporation Lexington, KY

Lexington, KY

2mo ago Closed

Full time Analytics Big Data Business Analytics

This position is no longer accepting applications (closed Aug 23, 2026).

Job Description

Lockheed Martin Corporation is seeking a skilled Data Engineer Staff to join its data engineering team in Lexington, Kentucky, on a hybrid work arrangement. The role centers on delivering end-to-end data solutions across the full data processing pipeline, designing, building, and operating scalable pipelines with an emphasis on security, performance, and DoD/SOFSA compliance.

Responsibilities

Build, test, and maintain robust data pipelines (batch & streaming) that clean, transform, and aggregate raw data into analytic ready data stores.
Implement data quality checks, smart caching, and virtualization techniques to guarantee consistency and reliability.
Build automated data management conduits that support machine learning data flows.
Collaborate with the Data Architect to create conceptual, logical, and physical data models that support business needs.
Define functional capabilities, security, backup, and recovery specifications for each solution.
Design and maintain systems that collect, store, and present data across the enterprise.
Conduct data profiling, requirement analysis, and performance tuning throughout the solution lifecycle.
Deploy, monitor, and optimize ETL/ELT processes (e.g., Informatica, Talend) and related reporting/analytics layers.
Develop and implement data mining and data warehousing programs to enrich the enterprise data ecosystem.
Identify and resolve production incidents; tune system parameters for optimal throughput and latency.
Evaluate, integrate, and validate new releases or platform upgrades.
Manage the compilation, cataloging, caching, distribution, and retrieval of data and maintain test data records.
Define and enforce best practice standards, certification processes, and metadata management policies for data objects.
Participate actively in the Data Governance Working Group.
Implement data management standards, requirements, and specifications and ensure compliance with data classification and handling rules through access control and security best practices.
Assist early stage risk identification and mitigation for task order activities.
Support Critical Design Reviews (CDRs) and advise Integrated Product Teams (IPTs) on data related risks and mitigation strategies.
Serve as the technical liaison to Systems Engineering, IPT engineers, and other disciplines to ensure complete, documented requirements allocation for IT and data systems.
Prepare and release engineering drawings and associated data system documentation.
Advise on and design the integration of new data technology solutions (e.g., IBM Cloud Pak for Data) into the SOFSA enterprise IT environment.
Advise higher level leadership on critical data management issues and emerging technology trends.
Report directly to the Data Architect and provide status updates on pipeline health, performance metrics, and risk registers.
Apply data acquisition, cleaning, transformation, and ingestion best practices for machine learning data conduits.
Assess and address methodological limitations to ensure reliable data delivery.
Read, interpret, write, modify, and execute simple scripts (e.g., PERL, VBS) on Windows and UNIX systems for parsing large files, automating manual tasks, and fetching/processing remote data.
Record, manage, and validate test data.
Establish data security controls, develop data dictionaries, and apply data mining techniques (e.g., file system searches).
Perform data pre processing (imputation, dimensionality reduction, normalization, transformation, extraction, filtering, smoothing).
Conduct transformation analytics (aggregation, enrichment, processing).

Requirements

Bachelor’s degree in Artificial Intelligence, Computer Science, Information Systems, Data Engineering, or a related field (Master’s preferred).
Data‑mining and data‑warehousing principles.
Database management systems, query languages, table relationships, and views.
Proficiency with Hadoop, Java, Python, SQL, Hive, and PIG for data exploration.
DataOps processes and best practices.
Data‑security roles and responsibilities; ability to implement access‑control and encryption safeguards.
Knowledge of how to collect, store, monitor, and validate test data.
Ability to assess methodological limitations and recommend mitigation strategies.

Technologies

Hadoop, Java, Python, SQL, Hive, PIG
Informatica, Talend, IBM Cloud Pak for Data
AWS, Azure, GCP
PERL, VBS
Microsoft Power BI Report Server, Deltek COBRA
Microsoft SharePoint, SAP, Teamcenter
PowerShell, Oracle SQL Server, Microsoft SQL Server

Benefits

Flexible Schedules
Competitive pay
Comprehensive benefits

Security Clearance

This position requires a government security clearance, you must be a US Citizen for consideration.

Clearance Level

Secret

Other Important Information

Expression of Interest: By applying to this job, you are expressing interest in this position and could be considered for other career opportunities where similar skills and requirements have been identified as a match. Should this match be identified you may be contacted for this and future openings.
Ability to Work Remotely: Part-time Remote Telework: The employee selected for this position will work part of their work schedule remotely and part of their work schedule at a designated Lockheed Martin facility. The specific weekly schedule will be discussed during the hiring process.
Work Schedules: Lockheed Martin supports a variety of alternate work schedules that provide additional flexibility to our employees. Schedules range from standard 40 hours over a five day work week while others may be condensed. These condensed schedules provide employees with additional time away from the office and are in addition to our Paid Time off benefits.
Schedule for this Position: Non- standard 40 hour work week as assigned by leader