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Lead Data Scientist: $105/hr

Infonet Consulting Group

Miami, FL · Posted October 10, 2026

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  • Python
  • Forecasting
  • SQL
  • Azure
  • CI/CD

About the job

Lead Data Scientist: $105/hr Position Now Available At Infonet Consulting Group Lead Data Scientist: $105/hr Apply Now! Job Code: 100926LDS Posted: 09-Oct-26 Location: Miami, FL Position Type: Full-time, Contract - Corp-to-Corp, Contract - Independent, Contract - W2 Rate: $105/hr 1099/Corp-to-Corp or $97/hr-W2 Job Length: 12 Months Telecommuting: Not Available Travel: Some Job Description One of Infonet's premier clients has an opening for a Lead Data Scientist. TERMS : Contract SCOPE OF WORK • Lead project teams through end-to-end data science delivery, ensuring models are technically sound, production-ready, and tied to adoption • 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 • Statistical and machine learning depth, combined with practical business judgment, strong stakeholder partnership, and the ability to convert analytical work into measurable outcomes rather than isolated prototypes • Frame high-impact business problems for project-level data science leadership into measurable data science opportunities with clear decision owners, baseline metrics, adoption paths, and expected value tied to project-level improvements in revenue, demand planning, digital conversion, operational efficiency, or customer experience • Develop forecasting, propensity, classification, and ranking models using Python, scikit-learn, XGBoost, LightGBM, CatBoost, and Databricks feature workflows to support production decisions • Build recommendation, simulation, and optimization solutions using MILP, heuristics, dynamic programming, or scenario modeling to improve operational and commercial decisions • 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 • 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 • Apply SHAP, sensitivity analysis, model diagnostics, error analysis, and stakeholder-ready explanations so users understand model behavior, limits, and decision implications • 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 • Monitor accuracy, drift, bias, adoption, latency, cost, and business KPIs; trigger retraining, recalibration, or process changes when performance or value realization degrades • Partner with business, product, operations, AI Engineering, and data engineering teams to convert model outputs into decisions, workflows, incentives, and measurable adoption • Explain model logic, uncertainty, tradeoffs, risks, and recommended decisions in business terms • Convert model outputs into decisions, workflows, incentives, and measurable adoption REQUIRED SKILLS / EXPERIENCE • Demonstrated experience appropriate to lead scope delivering ML, optimization, experimentation, or GenAI solutions that moved beyond analysis into production use or business decisioning • Hands-on experience with Python, scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch or TensorFlow where appropriate, and model evaluation workflows for production-grade use cases • Experience with MILP solvers, simulation, scenario planning, dynamic programming, heuristics, or prescriptive analytics methods applied to real business decisions • Experience with Azure AI Foundry, GPT-class models, RAG, embeddings, prompt engineering, evaluation, and safe use of GenAI for decision support or workflow automation • Advanced use of Databricks, Spark, SQL, feature pipelines, data quality checks, and reproducible analytical workflows for large-scale data science delivery • Experience with MLflow, Azure ML, model registries, CI/CD, monitoring, retraining, and production handoff practices that keep models reliable after launch • Strong Python engineering practices, Git workflows, testing, packaging, notebooks-to-production discipline, APIs, and collaboration with AI Engineering for deployment readiness PREFERRED 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 TRAVEL REQUIREMENT • Some – occasionally required to walk to the dock and ascend/descend the vessel • This position may require regular domestic or international travel ** No 3rd party vendors ** Unable to sponsor H1-B visas ** Please refer to position: 100926LDS - Lead Data Scientist: $105/hr in the subject line of all correspondence. Please select the "Apply Now" button. We look forward to reviewing your resume and speaking with you personally. Apply Now! Quick Links • Print this position • View more positions Why Infonet? Aggressive Rates - We only add a 10-15% markup. Transparency - We'll gladly let you know how much we're being paid. No Non-Compete Language - If we do our job well, you'll always want to stay with us. No Conversion Restrictions - You're free to accept a full-time position at any time. Your Approval Required - We need your approval to submit your resume. Discretion - We'll never contact your current employer without your approval. Lead Data Scientist: $105/hr Job Code: 100926LDS Posted: 09-Oct-26 Location: Miami, FL Position Type: Full-time Rate: $105/hr 1099/Corp-to-Corp or $97/hr-W2 Job Length: 12 Months Telecommuting: Not Available Travel: Some Job Description One of Infonet's premier clients has an opening for a Lead Data Scientist. TERMS : Contract SCOPE OF WORK • Lead project teams through end-to-end data science delivery, ensuring models are technically sound, production-ready, and tied to adoption • Data Scienc