Data Scientist

Data Scientist 6+ Month Contract Hybrid | Miami, FL POSITION SUMMARY We are seeking a Data Scientist to own the model lifecycle for defined business problems, from problem framing and feature development through deployment, monitoring, and adoption. This role emphasizes Data Science ownership of delivered business value: framing the right problem, building and validating ML/optimization/GenAI solutions, partnering on deployment, monitoring performance, and driving adoption in production. The ideal candidate combines statistical and machine learning depth with practical business judgment, strong stakeholder partnership, and the ability to convert analytical work into measurable outcomes rather than isolated prototypes. ESSENTIAL RESPONSIBILITIES • Problem Framing & Value: Frame high-impact business problems for independent end-to-end model delivery into measurable data science opportunities with clear decision owners, baseline metrics, adoption paths, and expected value tied to forecast accuracy, conversion, operational efficiency, guest experience, or revenue/profitability improvements. • Predictive Modeling: Develop forecasting, propensity, classification, and ranking models using Python, scikit-learn, XGBoost, LightGBM, CatBoost, and Databricks feature workflows to support production decisions. • Prescriptive Decisioning: Build recommendation, simulation, and optimization solutions using MILP, heuristics, dynamic programming, or scenario modeling to improve operational and commercial decisions. • GenAI Solutions: Design GenAI workflows using GPT-class models, Azure AI Foundry, RAG, embeddings, prompt engineering, and evaluation routines where natural-language or agentic capabilities improve business productivity. • Statistical Experimentation: Design and evaluate A/B tests, quasi-experiments, causal analyses, bootstrap methods, and non-parametric tests to determine whether model or process changes create measurable lift. • Explainability & Trust: Apply SHAP, sensitivity analysis, model diagnostics, error analysis, and stakeholder-ready explanations so users understand model behavior, limits, and decision implications. • Production Deployment: Partner with AI Engineering to deploy models and analytical applications through Databricks, Azure ML, MLflow, APIs, or containerized services while retaining accountability for business value and model behavior. • Production Operations: Monitor accuracy, drift, bias, adoption, latency, cost, and business KPIs; trigger retraining, recalibration, or process changes when performance or value realization degrades. • Stakeholder Partnership: Partner with business, product, operations, AI Engineering, and data engineering teams to convert model outputs into decisions, workflows, incentives, and measurable adoption. QUALIFICATIONS / KNOWLEDGE / SKILLS • Education: Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Operations Research, Engineering, Economics, or a related quantitative field, or equivalent practical experience. • Experience: Demonstrated experience appropriate to mid scope delivering ML, optimization, experimentation, or GenAI solutions that moved beyond analysis into production use or business decisioning. • ML Tooling: Hands-on experience with Python, scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch or TensorFlow where appropriate, and model evaluation workflows for production-grade use cases. • Optimization: Experience with MILP solvers, simulation, scenario planning, dynamic programming, heuristics, or prescriptive analytics methods applied to real business decisions. • GenAI Platforms: Experience with Azure AI Foundry, GPT-class models, RAG, embeddings, prompt engineering, evaluation, and safe use of GenAI for decision support or workflow automation. • Data Platforms: Advanced use of Databricks, Spark, SQL, feature pipelines, data quality checks, and reproducible analytical workflows for large-scale data science delivery. • MLOps: Experience with MLflow, Azure ML, model registries, CI/CD, monitoring, retraining, and production handoff practices that keep models reliable after launch. • Engineering: Strong Python engineering practices, Git workflows, testing, packaging, notebooks-to-production discipline, APIs, and collaboration with AI Engineering for deployment readiness. • Communication: Clear communication skills with business product owners, AI engineers, data engineers, and operational leaders, including the ability to explain model logic, uncertainty, tradeoffs, risks, and recommended decisions in business terms. Disclaimer: Brooksource, Medasource, and Calculated Hire are part of the Eight Eleven Group family of companies and operate under Eight Eleven Group, LLC. All employees receive the same benefits, policies, and terms of employment. EEO: We are committed to creating an inclusive environment for all employees and applicants. We do not discriminate on the basis of race, color, religion, creed, sex, sexual orientation, gender identity or expression, national origin, ancestry, age, disability, genetic information, marital status, military or veteran status, citizenship, pregnancy (including childbirth, lactation, and related conditions), or any other protected status in accordance with applicable federal, state, and local laws. Benefits & Perks: Brooksource offers competitive medical, dental, vision, Health Savings Account, Dependent Care FSA, and supplemental coverage with plans that can fit each employee’s needs. We offer a 401k plan that includes a company match and is fully vested after you become eligible, paid time off, sick time, and paid company holidays. We also offer an Employee Assistance Program (EAP) that provides services like virtual counseling, financial services, legal services, life coaching, etc. Pay Disclaimer: The pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.