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Senior Data Scientist

TechWish · New York, NY

5252

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Stale

Open 101 days · How we score

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  • Open 101 days

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Senior Data Scientist at TechWish. We’ll keep it in your applied jobs, so you can see it later and be warned before applying to the same employer twice.

Location

Mexico

Contract 3 months to start with.

Billrates, as per MSA. May be little flexible but not much.

Requirements for the Role

Senior Data Scientist – Key Requirements

  • 5–8 years of hands-on experience in data science, with a strong track record of delivering production-grade solutions in business environments.
  • Deep expertise in traditional machine learning techniques, including time series forecasting (e.g., ARIMA, SARIMA), regression models, and other statistical methods applied to real-world datasets.
  • Proven experience designing and implementing optimization models (e.g., linear programming, mixed-integer programming, heuristics) to solve complex business problems such as demand planning, inventory optimization, or resource allocation.
  • Recent, demonstrable experience working on Generative AI initiatives, including LLM-based solutions and agentic workflows (e.g., autonomous agents, multi-step reasoning pipelines, tool-augmented AI systems).
  • Hands-on experience in designing, deploying, and operationalizing agentic workflows in production environments, including orchestration, tool integration, monitoring, and performance optimization of AI-driven agents.
  • Strong domain knowledge in Consumer-Packaged Goods (Client), with hands-on experience in at least one functional area: Sales (e.g., demand forecasting, pricing), Finance (e.g., revenue/margin analytics), or Supply Chain (e.g., planning, logistics, inventory).
  • Ability to translate business problems into scalable analytical and AI-driven solutions, partnering closely with cross-functional stakeholders such as product managers, engineers, and business teams.
  • 100% hands-on role with active involvement in data exploration, model development, evaluation, deployment, and continuous improvement.
  • Proficiency in Python and relevant data science ecosystems (e.g., pandas, scikit-learn, statsmodels, PyTorch/TensorFlow), along with experience in cloud platforms and MLOps practices.

Skills

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