Data Scientist

Role: Data Scientist
Renton is highly preferred
Open to 100% on-site: St louis,75261- Dallas/ Plano 75024
ONLY OPEN TO W2 EMPLOYEES
NOT OPEN TO 1099 OR C2C
Must Have Skills:

· Bachelor’s degree in Data Science, Statistics, Computer Science, Economics, Engineering, or related field; advanced degree preferred.
· 7+ years of applied data science experience, with at least 5 years in Talent/People Analytics, or consulting for large enterprises.
· Demonstrated experience delivering end-to-end analytics and deploying models to production in cross-functional environments.
· Strong experience with HR systems and data models (Workday, PeopleSoft) or equivalent enterprise HR data experience.
· Modeling & methods: strong foundations in statistical modeling (linear/logistic regression, survival analysis/time-to-event where relevant), tree-based methods, clustering, causal methods, and applied NLP/transformer/LLM techniques for text- based HR applications.
· Programming: production-capable Python coding (modular design, testing, packaging) experience with version control (Git), and collaboration with DevOps/CI-CD workflows.
· Data engineering & infrastructure: experience working with ETL, feature engineering, data warehouses/lakes, and modern cloud platforms; familiarity with Spark, dbt, Airflow, or equivalents desirable.
· Model lifecycle & tooling: familiarity with model registries and lifecycle tools (MLflow, Seldon, Terraform/Helm or equivalent), explainability tools (SHAP, LIME), fairness/tooling (AIF360 or equivalent), and monitoring frameworks.
· Querying & visualization: advanced SQL skills; experience with BI/visualization tools (Tableau, Power BI) and producing executive-ready dashboards and narratives.
· Privacy & security: practical knowledge of de-identification, synthetic data, and access-control patterns for sensitive HR data.


Roles & Responsibilities

· Lead end-to-end analytic projects: define problem statements with HR stakeholders, design experiments, select appropriate methods, develop models, validate results, and deliver production-ready solutions and monitoring.
· Build predictive and prescriptive models for talent use cases (attrition/retention, internal mobility, promotion forecasting, performance indicators, recruitment sourcing/scoring, skilling/curation, compensation analytics).
· Develop and productionize features and models in collaboration with data engineers and ML engineers: implement reproducible ETL, feature pipelines, model training pipelines, CI/CD, and deployment patterns.
· Apply statistical methods, hypothesis testing, causal inference where appropriate, and robust validation (cross-validation, holdouts, calibration, fairness testing) to ensure reliable, defensible results.
· Design and operationalize NLP/LLM solutions for HR use cases (resume parsing, candidate experience, employee feedback analysis) while enforcing privacy, data minimization and explainability requirements.
· Instrument model monitoring and drift detection; define alerting, retraining triggers, and remediation plans.
· Produce clear, actionable visualizations and dashboards that tell the story of analytic findings and drive decisions; collaborate with BI developers to operationalize reporting.
· Translate technical analyses into business recommendations, quantify expected impact, and work with partners to implement changes and measure outcomes.
· Mentor junior data scientists/analysts, review code and model artifacts, and help raise team standards for reproducibility, documentation, and governance.
· Ensure models and data products adhere to governance, privacy, and ethical requirements; collaborate with HR Data Steward, Legal/Privacy, and Ethics/AI governance on reviews and approvals.


Managerial Skills:

· Problem-solver with product mindset: frames analytics as business products with clear KPIs and adoption plans.
· Ownership & results orientation: takes accountability for delivery, end-to-end operation, and measurable impact.
· Communication & storytelling: synthesizes complex analyses into concise recommendations for HR leaders and executives.
· Collaboration & influence: builds strong cross-functional relationships and navigates competing priorities.
· Coaching & development: mentors peers and contributes to team capability growth.
· Ethical judgment: prioritizes fairness, privacy, and employee impact in modelling decisions