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4 jobs found

11d ago

Data Scientist (Mid-level) - Risk Modeling

USAA San Antonio, TX

San Antonio, TX

11d ago

$114k - $218k/yr Full time Artificial Intelligence Data Data Analysis

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Job Description

USAA is hiring a mid-level Data Scientist for the P&C Underwriting Data Science team, focused on risk modeling and the delivery of data science products into day-to-day operations. The role combines hands-on experimentation with statistical prototyping, production deployment, and model risk management practices within the Model Development Control and Model Risk Management frameworks.

Based in San Antonio, TX, this onsite position involves turning business requests into analytical questions, building and deploying models using machine learning, simulation, and optimization, and communicating results to both technical and non-technical stakeholders.

The Opportunity

Experiment with data, prototype statistical models, and deploy new data science products in operations for P&C Underwriting.
Build and deploy risk and analytics models and solutions using machine learning, simulation, and optimization.

Responsibilities

Gather, interpret, and manipulate structured and unstructured data to enable advanced analytical solutions.
Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and value.
Select appropriate modeling techniques and/or technologies based on data limitations, the intended application, and business needs.
Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) frameworks.
Compose technical documentation for knowledge persistence, risk management, and technical review audiences.
Assess business needs to propose or recommend analytical and modeling projects that add business value.
Participate in prioritization of analytics and modeling problems and research efforts with business and analytics leaders.
Contribute to a robust library of reusable, production-quality algorithms and supporting code to support transparency and high-quality data practices.
Translate business requests into specific analytical questions, complete analysis and/or modeling, and communicate outcomes to non-technical business partners with emphasis on actions and recommendations.
Work with Data Engineering, IT, the business, and internal stakeholders to deploy production-ready analytical assets aligned to customer vision and specifications while meeting modeling best practices and model risk management standards.
Maintain awareness of cutting-edge techniques.
Actively seek opportunities and materials to learn new techniques, technologies, and methodologies.
Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled according to risk and compliance policies and procedures.

Requirements

Bachelor's degree in Mathematics, Computer Science, Statistics, Economics, Finance, Actuarial Science, Science, Engineering, or a quantitative field; OR 4 years of relevant education and/or experience.
4 years of experience in predictive analytics or data analysis OR an Advanced Degree (such as a Master’s or PhD) in a quantitative discipline plus 2 years of experience in predictive analytics or data analysis.
2 years of experience training and validating statistical, physical, machine learning, and other advanced analytics models.
2 years of experience using a dynamic scripted language (such as Python or R) for statistical analyses and/or building and scoring AI/ML models.
Experience writing code that is easy to follow, well documented, and commented where necessary for logic with high code transparency.
Experience querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, or NoSQL.
Experience working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
Experience conducting ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
Ability to assess regulatory implications and expectations for distinct modeling efforts.
Experience with classical supervised modeling concepts and technologies for prediction, including linear/logistic regression, discriminant analysis, support vector machines, decision trees, and forest models.
Experience with unsupervised modeling concepts and technologies such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, and DBSCAN.
Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.

Technologies

Python, R, SQL, HQL, NoSQL
JSON, XML
Linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models
K-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN
Machine learning, simulation, optimization, AI/ML
Model Development Control (MDC), Model Risk Management (MRM)

Compensation

Salary range: $114,080 - $218,030 per year.
Pay is based on experience and market data; the actual salary may vary by location.
Employees may be eligible for pay incentives based on overall corporate and individual performance, at the discretion of the USAA Board of Directors.

Benefits

Comprehensive medical, dental and vision plans
401(k)
Pension
Life insurance
Parental benefits
Adoption assistance
Paid time off program with paid holidays plus 16 paid volunteer hours
Various wellness programs
Career path planning and continuing education

Work Environment

Flexible work environment requiring in-office presence 4 days per week.
Position locations include San Antonio, TX; Phoenix, AZ; Plano, TX; Tampa, FL; or Colorado Springs, CO.
Relocation assistance is not available for this position.

Additional Information

VISA sponsorship: USAA does not provide visa sponsorship for this role.
Applications are accepted on an ongoing basis and the posting remains open until filled.
Equal opportunity employer.

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30d ago

Machine Learning Engineer

Mariana Minerals Ann Arbor, MI

Ann Arbor, MI

30d ago Closed

$120k - $180k/yr Full time Ai Ml Artificial Intelligence Engineer

This position is no longer accepting applications (closed Sep 9, 2026).

Job Description

Mariana Minerals is building machine learning systems that control mineral refining facilities, with a strong focus on reinforcing learning in physically realistic simulators and closing the gap to real plant data. This is an onsite role in Ann Arbor, MI, where your work moves from experimentation to production. The salary range is $120,000 - $180,000 per year.

What you’ll do

Run reinforcement learning experiments in physically realistic simulators of mineral processing operations, then translate results into improved controllers.
Build and refine training environment components, including reward functions, observations, and action logic, with guidance from senior engineers.
Train control models, track and interpret performance, and investigate why a model underperforms.
Close the loop between simulation and reality by comparing model behavior to real plant data and flagging where simulated physics diverge.
Write clean, well-tested code and contribute to the services that deploy models into production.
Partner with process and chemistry experts to understand the unit operations being modeled.

What you bring

2-8+ years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing, or a strong recent graduate with demonstrated project depth.
Solid grounding in machine learning fundamentals, with working knowledge of modern deep learning; reinforcement learning experience is a strong plus.
Proficiency in Python and comfort reading and debugging an existing codebase.
Curiosity about physical, industrial systems and willingness to learn chemistry and process engineering from experts who challenge assumptions.
A self-starter who asks good questions, ships, and escalates blockers early.

How the technology is used

The stack uses the same reinforcement learning toolkits that power self-driving vehicles and humanoid robots, adapted for autonomous, short-interval control of mineral refining circuits. Models adjust operating set points and configurations in real time, optimizing across lithium recovery, reagent consumption, energy intensity, and equipment uptime simultaneously. Training happens in physically realistic simulators of process units, then performance is validated against real plant data before anything is introduced to live equipment.

Why this matters (and why you’ll like it)

We own the projects, generate the data, and close the loop.
Each facility helps make the software smarter, and the next one faster and cheaper.
Mining is one of the last major industrial sectors that hasn’t been rebuilt with modern software.
Your work directly shapes how critical minerals are produced at scale in the coming decades.

Culture at Mariana Minerals

Extreme Ownership – take full responsibility for outcomes and drive toward solutions.
Engineer Out Requirements, then Automate – simplify, optimize, and automate for scale.
Share Your Legos – collaborate openly, share knowledge, and empower each other to build bigger, better solutions.

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1mo ago

Sr. Data Scientist, Ops Research

MCKESSON Irving, TX

Irving, TX

1mo ago Closed

$136k - $227k/yr Full time Azure Ml Big Data Cloud Platform

This position is no longer accepting applications (closed Sep 6, 2026).

Job Description

Build stochastic process simulation and optimization products to improve supply chain operations within McKesson’s Enterprise Data Science team.

Responsibilities

Develop and apply digital twins and simulation/optimization frameworks to support decisions across supply chain areas including inventory, transportation, and labor planning.
Translate simulation and optimization outputs into actionable recommendations for business partners.
Architect, implement, and drive adoption of stochastic process simulation and optimization solutions; measure impact and make significant improvements to existing solutions.

Requirements

Demonstrated experience developing stochastic process simulations to inform business decisions in inventory, transportation, or other supply chain-related domains.
Strong foundation in probability and statistics, including random variables, probability distributions, hypothesis testing, regression, and modern machine learning methods.
Demonstrated experience in data wrangling using SQL.
Experience with statistical modeling in Python and/or R.
Ability to communicate results to technical leaders and non-technical executive audiences.
Degree or equivalent, typically requiring 7+ years of relevant experience.

Tools & Technologies

SQL, Python, R
Optimization solvers: CPLEX, Gurobi, Xpress, CBC, GLPK
Data and ML platforms: Databricks, Snowflake, Azure ML

Preferred Skills

Experience with commercial or open-source optimization solvers (e.g., CPLEX, Gurobi, Xpress, CBC, GLPK).
Familiarity with reinforcement learning or approximate dynamic programming techniques.
Experience developing dashboards, applications, or decision-support tools that expose model outputs to business users.
Exposure to financial modeling, cost optimization, or pricing analytics.
Experience working in modern data and ML platforms such as Databricks, Snowflake, and Azure ML.

Location & Compensation

Location: Irving, TX (onsite)
Base pay range: USD 136,300 - 227,100 per year

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1mo ago

Senior Machine Learning Engineer, End‑to‑End Autonomous Driving

NVIDIA Santa Clara, CA

Santa Clara, CA

1mo ago Closed

$184k - $357k/yr Full time Senior Active Learning Artificial Intelligence

This position is no longer accepting applications (closed Aug 13, 2026).

Job Description

NVIDIA in Santa Clara is seeking a Senior Machine Learning Engineer to design, train, and deploy end-to-end autonomous driving models while building data-centric pipelines and data flywheels that accelerate development. This onsite role involves close collaboration with researchers and engineers to convert advanced research into robust, production-ready ML systems for autonomous vehicles.

Responsibilities

Design, implement, and train scalable end-to-end driving models for autonomous vehicles.
Drive the data flywheel by identifying failure modes, defining data collection and labeling needs, and iterating models to close real-world performance gaps.
Build, curate, and maintain high-quality multimodal datasets including video, sensor streams, and language/action traces for end-to-end driving.
Develop and apply data-centric learning strategies such as active learning, curriculum learning, automated hard-example mining, outlier and novelty detection, and semi/self-supervised methods.
Explore and productize new data sources, including simulation, synthetic data, and world-model-based generation or augmentation to improve coverage and robustness.
Design and implement automated data workflows that handle discovery, labeling, evaluation, and retraining to maximize development velocity.
Foster collaborative partnerships with researchers and engineers to transform innovative research into robust, production-ready ML models.

Requirements

PhD with 4+ years, MS with 6+ years, or BS (or equivalent) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related field.
Strong background in modern deep learning, including transformer architectures, video modeling, and multimodal VLM/VLA or foundation models.
Hands-on experience training and deploying deep learning models on real-world datasets, covering preprocessing, distributed training, evaluation, debugging, and iterative improvement.
Practical experience with data-centric methods such as active learning, curriculum learning, outlier/novelty detection, or large-scale sample mining.
Proficiency in Python and at least one major deep learning framework (PyTorch, TensorFlow, or JAX), plus solid software engineering practices (testing, code reviews, CI/CD).
Demonstrated ability to collaborate across teams, drive designs from prototype to production, and communicate with technical and non-technical partners.
Track record of leading complex cross-team projects, setting technical direction, and making critical technical decisions that impact multiple teams or products.

Technologies

Python
PyTorch
TensorFlow
JAX

Benefits

Equity
Benefits

Ways to stand out from the crowd

Experience building and operating data flywheels or large-scale data pipelines for ML, including data quality monitoring and continuous retraining loops.
Direct experience with end-to-end driving models, large-scale behavior cloning, or reinforcement/imitation learning for driving or robotics.
Experience leveraging simulation, synthetic data, or world models to generate training and evaluation data for autonomous systems.
Contributions to advanced data-centric ML methods, VLM/VLA, or autonomous driving through publications, open-source projects, or widely used internal tools.
Background with safety, reliability, and validation requirements for autonomous driving or other safety-critical applications.

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Simulation Data Jobs

Data Scientist (Mid-level) - Risk Modeling

Machine Learning Engineer

Sr. Data Scientist, Ops Research

Senior Machine Learning Engineer, End‑to‑End Autonomous Driving

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