Data Scientist

The Data Scientist serves as a solution developer within the North America Regional Product-Oriented Delivery (POD) team, designing and validating advanced analytical and machine learning solutions that deepen understanding of the business, identify opportunities, and solve complex problems. The role translates business challenges into mathematical and statistical models, works with Data Engineering and IT partners to prepare enterprise data, and partners with Enterprise Technology AI Platform, MLOps, Architecture, Security, Compliance, and Operations teams to move governed solutions through the AI/ML lifecycle.

Details

Making an Impact

• Design and create experimental analytical and machine learning solutions that quantify variable impacts on desired business outcomes using statistical, mathematical, and AI techniques.

• Translate complex business problems into predictive, prescriptive, optimization, and decision models that can be implemented, validated, and automated.

• Work with IT partners and Data Engineers to define data requirements and create integration and preparation pipelines that merge large structured and unstructured datasets for advanced analytics.

• Lead model design activities, including algorithm selection, feature engineering, experiment design, model training, hyperparameter tuning, testing, validation, and performance optimization.

• Design and build predictive models using principles that enhance traceability, reproducibility, relevance, explainability, and trustworthiness.

• Apply responsible AI controls, including documented evaluation criteria, robustness testing, bias and fairness assessment, explainability, and validation reporting.

• Create documentation supporting business justification, model design, validation, model cards, lineage, governance review, and audit evidence.

Sharing Expertise

• Proactively identify and frame critical, yet undefined, business problems as measurable analytical or AI use cases.

• Provide data science expertise to the North America Regional POD and clearly communicate analytical methods, assumptions, limitations, and recommendations.

• Develop reusable analytical assets, code, documentation, and practices that accelerate delivery and support consistent model quality.

• Transform data science insights into scalable analytical products and decision-support capabilities for business functions.

Gaining Exposure

• Collaborate with business leaders, product owners, Data Engineers, architects, and cross-functional partners of varying technical levels.

• Work within the Enterprise AI Industrialization framework with Enterprise Technology AI Platform, MLOps, DevOps, Security, Compliance, Architecture, Infrastructure, and Operations teams.

• Participate in solution architecture, governance, production-readiness, user acceptance testing, production validation, and post-deployment performance discussions.

• Translate complex findings and model results into a compelling narrative for non-technical stakeholders and decision makers.

Your Typical Day

• Partner with North America stakeholders to define AI and advanced analytics use cases, expected business value, success criteria, data needs, assumptions, and risks.

• Explore, prepare, and analyze large datasets; engineer features; design experiments; and program statistical, machine learning, and optimization models.

• Collaborate with Data Engineering and IT teams on approved data acquisition, integration, quality, preprocessing, metadata, and lineage requirements.

• Develop, test, validate, tune, and document models, including experiment results, performance thresholds, explainability, bias and fairness considerations, and model limitations.

• Partner with Enterprise Technology AI Platform and MLOps teams on environment readiness, versioning, CI/CD enablement, deployment requirements, monitoring configuration, and governed production promotion.

• Participate in user acceptance testing and production validation; review model performance, drift or degradation alerts, and retraining or issue-resolution needs with Operations and governance partners.

• Maintain model design documentation, validation reports, model cards, audit evidence, and other lifecycle artifacts required by enterprise standards.

• Travel 5% or less.

Other accountabilities as assigned

Leverage and effectively use AI-enabled tools, technologies, and digital solutions, consistent with organizational policies and role requirements, to enhance effectiveness, efficiency, and decision-making. Apply appropriate human judgment, accountability, and ethical considerations in all technology-supported work in alignment with our Human First, Digital Always philosophy, A strong commitment is made by each employee and is necessary to ensure equal employment opportunity for all. ManpowerGroup is an inclusive workplace that will recruit, hire, train, and promote persons of all job titles, and ensure all other personnel actions are administered without regard to non-merit-based characteristics of individuals.

• 3 years of relevant experience in Data Science, Machine Learning, Applied Statistics, Operations Research, Advanced Analytics, or a closely related field.

• Technical: Proficiency in SQL and programming techniques and tools (Python, R); Cloud computing platforms: Azure, Snowflake; Machine Learning and Advanced Analytics: predictive modeling, classification, regression, clustering, feature engineering, experiment design, model validation, and performance optimization; NLP skills including tokenization, sentiment analysis, and embeddings; Model Optimization: model tuning, hyperparameter adjustment, explainability, bias and fairness testing, reproducibility, monitoring, drift assessment, and model lifecycle management; Experience collaborating with Data Engineering, IT, Architecture, DevOps/MLOps, Product, and business stakeholders within Agile or Product-Oriented Delivery (POD) environments

• Education: Bachelor’s degree in Economics, Statistics, Mathematics, Computer Science, Data Science, Operations Research, or related quantitative field, or equivalent experience

Nice to Have

• Graduate degree in data science, statistics, computer science, economics, operations research, or another quantitative field.

• Experience with Azure Machine Learning, MLflow, Azure DevOps, containerized deployment patterns, or model observability tooling.

• Experience with LLMs, Generative AI, embeddings, retrieval-augmented generation, or related evaluation practices.

• Experience producing model cards, algorithm validation reports, governance submissions, or audit-ready AI documentation.