td>| Duties: | Responsible for the exploration, aggregation, transformation, and cleansing of vehicle data across a variety of disparate sources and platforms, ensuring data integrity and consistency at scale; design, implement, and continuously optimize advanced predictive machine learning models to drive accurate and actionable business forecasts, leveraging complex datasets; architect, develop, and maintain high-performance APIs in Python, ensuring seamless integration with diverse data ecosystems while adhering to best practices for scalability and security; support efforts to streamline and automate the data pipeline, encompassing data acquisition, feature engineering, model development, and deployment workflows, optimizing for performance, efficiency, and scalability; coordinate and execute complex ad-hoc data analysis tasks, providing rapid, data-driven insights for immediate business needs; provide mission-critical on-call support to ensure the continued operation of business production systems, troubleshooting and resolving issues with minimal impact to operations; utilize and apply knowledge of Python, SQL, Scikit-learn, XGBoost, Prophet, ARIMA, RESTful APIs, GCP, Terraform, Docker, VBA, and ODBC to complete assignments; translate complex analytical findings into clear, actionable insights; utilize cutting-edge Natural Language Processing (NLP) techniques to extract valuable insights from large volumes of unstructured text data, integrating AI-driven solutions to deliver sophisticated data analysis that directly impacts automotive operations and strategic initiatives; apply advanced statistical methodologies including Regression Analysis, Bayesian Inference, and machine learning-based forecasting techniques, to model and predict complex variables like market incentives, inventory management, sales forecasting, and operational performance; provide data-driven insights to optimize production strategies and facilitate high-level decision-making; leverage Cloud Computing platforms (primarily GCP) to architect and scale infrastructure for processing, storing, and analyzing massive automotive datasets; deploy data science solutions that integrate seamlessly with manufacturing and operational environments to drive efficiency, accuracy, and business intelligence; and present models and results to stakeholders, including business executives, to influence strategic decision-making. Education: Master’s – Data Science, Computer Science, Computer Engineering, Systems Engineering, or in a related field of study (will accept equivalent foreign degree); Training: None; Experience: One (1) year in the position above, as a Data Analyst, Data Engineer, as a Data Engineering Specialist, or in a related occupation; Other Requirements: Experience must include one (1) year use of all the following: Python, SQL, Scikit-learn, XGBoost, Prophet, ARIMA, RESTful APIs, GCP, Terraform, Docker, VBA, and ODBC.Battle Creek, MI13 days ago FOREIGN EDUCATION: If you are using education completed in foreign colleges or universities to meet the qualification requirements, you must show that the education credentials have been evaluated by a private organization that specializes in interpretation of foreign education programs and such education has been deemed equivalent to that gained in an accredited U.S. education program; or full credit has been given for the courses at a U.S. accredited college or university. Provides professional and scientific expertise in the application of data science disciplines for complex studies in machine learning and deep learning algorithms, statistical analysis, visualizations, programing and computer science. Working with a team of interdisciplinary data scientists, engineers, architects, and consultants, our work includes novel areas such as cancer detection, drug discovery, optimizing population health and clinical trials, autonomous systems and edge AI, agentic solutions, and consumer product innovation. From strategy to execution, this team delivers integrated, end-to-end support and advisory services covering valuation modeling, cost optimization, restructuring, business design and transformation, infrastructure and real estate, mergers and acquisitions (M&A), and sustainability. Working with a team of interdisciplinary data scientists, engineers, architects, and consultants, our work includes novel areas such as cancer detection, drug discovery, optimizing population health and clinical trials, autonomous systems and edge AI, agentic solutions and framework design, and consumer product innovation. From strategy to execution, this team delivers integrated, end-to-end support and advisory services covering valuation modeling, cost optimization, restructuring, business design and transformation, infrastructure and real estate, mergers and acquisitions (M&A), and sustainability. Southgate, Washington13 days ago and/or transports placardable amounts of hazardous materials by ground in any vehicle on a public road while in commerce, may be subject to additional Federal Motor Carrier Safety Regulations including: Driver Qualification Files, Medical Certification (obtained before onboarding), Road Test, Hours of Service, Drug and Alcohol Testing (CDL drivers only), vehicle inspection requirements, CDL requirements (if applicable) and hazardous materials transportation/shipping training. Required for Certain Job Profiles: Drivers who operate Commercial Motor Vehicles with a Gross Vehicle Weight (GVW), Gross Vehicle Weight Rating (GVWR) or combination of power unit and trailer that meets or exceeds 10,001 lbs. SUMMARY AF Group is seeking a Principal Data Scientist with expertise in either Commercial Property or Personal Homeowners insurance to serve as an individual contributor and technical authority on applying advanced analytics and machine learning to complex business problems, including pricing, risk selection, and other underwriting challenges. The Principal Data Scientist ensures long-term model performance through rigorous validation, drift monitoring, and audit-ready documentation, while advancing analytical best practices and evaluating emerging techniques relevant to commercial P&C insurance. Warren, Michigan17 days ago To be considered for this role, you must have at minimum a PhD with 2 years, or Masters with 5 years of relevant industry experience in one of the following or otherwise related quantitative fields: Computer Science, Computational Biology, Systems Biology, Quantitative Biology, Bioinformatics, Data Analytics, Biostatistics. Regeneron’s Precision Medicine Quantitative Biomarker team (PMQBS) is seeking a Principal Scientist with a blend of deep biomarker and translational-science expertise coupled with advanced computational and analytical skills. Demonstrated experience owning end-to-end model lifecycle, including validation, monitoring, and performance optimization Experience mentoring junior data scientists or leading analytical workflows preferred Aptitude and willingness to learn new things and teach others Strong written and oral communication skills. Mathematics, Statistics, Computer Science, Physics, Operations Research, Economics, Electrical Engineering, etc.) Proficiency in SQL or other data querying language An understanding of statistical and machine learning techniques, including classification, regression, clustering, feature engineering, decision trees, gradient boosting, deep learning, etc. p>•6 plus years work experience as a data scientist preferably in healthcare environment but candidates with suitable experience in other industries will be considered •Knowledge of big data technologies (e.g., Hadoop, Spark) •Familiar with relational database concepts, and SDLC concepts •Demonstrate critical thinking and the ability to bring order to unstructured problems •Technical Proficiency: Strong programming skills in languages such as Python and R, and experience with machine learning frameworks like TensorFlow, Keras, or PyTorch. You will work at the intersection of LLMs, systems engineering, and applied ML, building intelligent agents that reason, plan, interact with tools, and operate reliably in real-world environments-particularly across regulated domains such as healthcare. Grand Rapids, MI19 days ago Preference for at least one of the following fields of study: Management Information Systems, Computer and Information Science, Systems Engineering, Mathematics, Engineering, Electrical Engineering, Chemical Engineering, Industrial Engineering, Mathematics, Statistics, or Mathematical Statistics, Data Processing/Analytics/Science, Artificial Intelligence and Robotics. At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials. Grand Rapids, MI19 days ago Preference for at least one of the following fields of study: Management Information Systems, Computer and Information Science, Systems Engineering, Mathematics, Engineering, Electrical Engineering, Chemical Engineering, Industrial Engineering, Mathematics, Statistics, or Mathematical Statistics, Data Processing/Analytics/Science, Artificial Intelligence and Robotics. At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials. li>Develops complex machine learning models and updates existing models to validate effectiveness for various clients using techniques such as regression, decision trees, random forests, artificial neural nets, survival analysis, and/or time series. - University degree in an appropriate technical or analytical field required (i.e., computer science, engineering, applied mathematics, statistics, econometrics, analytics, etc.) from an accredited college or university, or equivalent foreign institution; Masters degree in a related field preferred.
This role emphasizes end-to-end backend solution development, cloud-native architecture, and operational deployment using AWS services such as Lambda, DynamoDB, OpenSearch, Neptune, Bedrock, SageMaker, etc. Responsibilities:Under limited supervision, the AI Engineer is responsible for designing, developing, and deploying production-ready AI and machine learning solutions that address complex business needs. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high-stakes legal work where accuracy, trust, and accountability are critical. We work closely with engineering, product, design, data engineering, machine learning operations, and LLM engineering teams to translate complex AI research into production-ready features used by legal professionals around the world. Auburn Hills, MI30+ days ago The ideal candidate combines strong analytical skills with practical experience building scalable analytics, models, and data products in enterprise environments. You will be part of a talented team driving predictive analytics and early detection of emerging quality trends using vast datasets across the enterprise. Auburn Hills, MI30+ days ago Data scientists work closely with data engineers, analysts, and business teams to design analytics solutions, implement advanced algorithms and evaluate the performance of use cases. Develop and validate predictive models using techniques such as regression, random forests, gradient boosting, causal modeling and neural networks. Auburn Hills, MI8 days ago p>This is a high-impact, senior IC role (Staff/Principal level influence) responsible for shaping technical strategy, designing scalable ML systems, and driving measurable business outcomes such as quality improvement, warranty reduction, and customer experience enhancement. A minimum of 8 years of experience in data science, advanced analytics, or machine learning, including a minimum of 5 years of hands-on experience with Databricks, Palantir, Snowflake, or AWS SageMaker. Okemos, Michigan30+ days ago Position requires experience in statistical modeling methods, machine learning algorithms, supervised and unsupervised learning, and data mining techniques; proficiency with statistical software packages like SAS, R, or equivalent preferred; experience with programming languages, especially Python and SQL, or equivalent; excellent communication skills and the ability to clearly and concisely communicate complex information to a variety of audiences, both technical and non-technical; the ability to collaborate in a team. To implement quantitative and predictive models, data science experiments using literate programming techniques such as Python Jupyter Notebooks, develop appropriate visualizations of data, curate and prepare data using appropriate programmatic and non-programmatic data transformation techniques, continuously learn new techniques and methods to improve data science outcomes. Ten (10) years of related experience or equivalent combination of education and experience to include three (3) years of predictive modeling, data science, and analysis experience with solid background in the using data visualization tools and libraries and data exploration, data wrangling, and feature engineering. PRIMARY PURPOSE OF THE ROLE: To partner with stakeholders to identify, scope, and execute advanced analytics and data science projects; to design data modeling processes, create algorithms, and predictive models; and to share insights that inform and enable business decisions. The Role: As an Artificial Intelligence and Machine Learning Scientist, you'll be part of a team that is pioneering the integration of simulation, automation, AI agents, large language models (LLMs), and machine learning into critical systems for vehicle design, calibration, and performance. Work collaboratively with a team of specialists ranging from data scientists, simulation experts and calibration technical specialists to cohesively build new capabilities into our existing Co-Simulation (Digital Twin) framework. Employer will accept a Master's degree or equivalent in Statistics, Mathematics, Computer Science, Computer Engineering or related quantitative field of study and 1 year of experience in Data Science and Statistics related to business in lieu of a Bachelor's degree or equivalent and 5 years of progressive experience. 4) statistical techniques and concepts, including regression, probability, distributions, and statistical testing with hands-on experience applying them to real-world problems (such as A/B testing, forecasting, and predictive modeling) using tools including Python, R, or SA. Farmington Hills, Michigan30+ days ago You will work to analyze human factors, safety management, and risk communication issues in a wide variety of scenarios involving consumer products, electronics, chemical products, manufacturing, oil & gas production, environmental exposures, and more. For over five decades, we've connected the lessons of past failures with tomorrow's solutions to advise clients as they innovate technologically complex products and processes, ensure the safety and health of their users, and address the challenges of sustainability. Data Scientist Sedgwick Claims Management Services, Inc.Data ScientistMI30+ days ago Mental: Clear and conceptual thinking ability; excellent judgment, troubleshooting, problem solving, analysis, and discretion; ability to handle work-related stress; ability to handle multiple priorities simultaneously; and ability to meet deadlines. Partners with both business and IT leaders to influence how the organization approaches and meets business challenges of an evolving customer base and changing marketplace, using strong business acumen. Grand Rapids, MI20 days ago Responsibilities:Follow the laboratory's procedures for specimen handling and processing, test analyses, reporting and maintaining records of patient test result; Maintain records that demonstrate that proficiency testing samples are tested in the same manner as patient specimens; Analyze specimens using approved testing procedures (see department SOPs). Adhere to the laboratory's quality control policies, document all quality control activities, instrument and procedural calibrations and all maintenance performed; Follow the laboratory's established policies and procedures whenever test systems are not within the laboratory's established acceptable levels of performance. Position Purpose: The Senior Data Scientist will support R&D efforts in bio-polymers and sustainable materials and focusing on applying advanced data science, statistical modeling, and machine learning to experimental, process, and materials data to accelerate innovation, improve material performance, and reduce development cycles. From tapes and films to packaging and protective products, as well as engineered coated materials and advanced packaging machinery, we develop innovative solutions that protect the world. Required Skills: Algorithms, Data Analysis, Machine Learning (ML), Natural Language, Python (Programming Language), Reinforcement Learning, Researching, Scientific Writing, Statistical Models, Technical Leadership. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high‑stakes legal work where accuracy, trust, and defensibility are essential. Southfield, MI30+ days ago This role will work across business functionssuch as Manufacturing, Supply Chain, Finance, Engineering, etc. to deliver scalable analytics, predictive models, and AI solutions that drive operational efficiency, quality, and business performance. JOB SUMMARY: Under moderate supervision, the Data Scientist applies advanced statistical, machine learning, and artificial intelligence techniques to solve complex business problems across the enterprise. The Data Scientist delves into the recesses of large data sets of structured, semi-structured and unstructured data to discover hidden knowledge about our business and to develop methods to leverage that knowledge within our line of business. As the Data Scientist, you'll be responsible for performing exploratory data analysis, feature engineering and predictive modeling to provide actionable insights and strategic direction for business leaders. p>This is a strong fit for someone who wants to be a hands-on senior individual contributor: someone who can independently own analytics for a product pillar, initially focusing on the highest-priority products within that pillar, influence roadmaps through data, and help build strong analytical habits within a growing team. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more. p>MLOps & Pipelines: Ensure the team builds robust, automated pipelines for data ingestion, model training, deployment, monitoring, and retraining in production environments. In this hands-on leadership role, you will bridge the gap between technical execution and strategic goals, driving the development of advanced algorithms for predictive demand forecasting, dynamic segmentation, and yield optimization. li>Cloud & Big Data Platforms: (Preferred Microsoft Azure (Data Lake, Machine Learning, Databricks), Nice to Have (AWS (S3, SageMaker, Bedrock) or Google Cloud Platform (BigQuery, Dataflow, AI Platform) ). Machine Learning & AI: Large Language Models (LLMs), Generative AI, RAG, Generative AI, Deep learning, Reinforcement Learning, Natural Language Processing (NLP), SVM, XGBoost, Random Forest, Decision Trees, Clustering. Ann Arbor, MI30+ days ago About You Youre a fit for the role of Applied Scientist if your background includes: • PhD or Masters degree in Computer Science, Machine Learning, or a related field • 1-2 years of hands-on experience building systems using modern techniques in information retrieval, NLP, machine learning, or generative AI (examples include deep learning, transformer architectures, hybrid search, dense retrieval, vector databases, or agentic systems) • Strong programming skills in Python and familiarity with a modern ML framework (PyTorch, JAX, DeepSpeed, or similar) • Experience working with shared codebases and version control systems • Familiarity with cloud development environments (AWS, Azure, or GCP) • Strong communication and problem-solving skills, with the ability to work effectively across functions • Experience implementing and evaluating solutions with large language models and LLM evaluation frameworks (e.g., OpenAI Evals, HELM, LM Harness, or custom tools) • Experience with retrieval-augmented generation (RAG), tool-using agents, and agentic frameworks • Publications or preprints in relevant venues (NeurIPS, ACL, EMNLP, ICLR, SIGIR) • Experience with production code and MLOps practices #LI-MW1 Whats in it For You? • Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing. The Center for Cutaneous Biology and Immunology (CCBI) is a dynamic, multidisciplinary research program dedicated to advancing our understanding of skin biology and immunology, cancer immunology, and the functional genomics that govern immune cell behavior in cancer as well as autoimmune and inflammatory diseases. The successful candidate will contribute to high-impact translational research programs and lead independent research efforts, and will hold a joint faculty appointment (Assistant Scientist) with Michigan State University as part of the HFH-MSU Health Sciences partnership. Auburn Hills, MI30+ days ago This role is ideal for someone who can operate at both the strategic and technical levels: designing decision frameworks, influencing cross-functional partners, and building models that materially improve cost, service, and resilience. Deep experience with predictive modeling (statistical, ML, deep learning), optimization techniques (LP, MIP, constraint programming), and simulation methods (Monte Carlo, discrete event). |