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TechWish · New York, NY

Senior Data Scientist

seniorfull timePosted Aug 14
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Stack mentioned

llmagentic-aipythonpandaspytorchtensorflowmlopsdata-sciencemachine-learningtime-seriesgenerative-ai

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.

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