Lead Data Scientist - Solution Architect

Role overview

A senior architect-level data scientist is needed to lead complex data science, AI, machine learning, and advanced analytics engagements for manufacturing-sector clients. The role blends hands-on technical depth with architectural leadership, client-facing consulting, and direct mentorship of a more junior data scientist.

Responsibilities

- Serve as the architectural lead across data science, AI, ML, forecasting, optimization, and advanced analytics projects, owning the end-to-end technical direction. - Partner with clients to understand business problems, gather requirements, identify data limitations, and translate needs into scalable Azure-based solutions. - Design and guide production-ready AI/ML solutions using Azure Machine Learning, Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Synapse Analytics, Databricks, Spark, and Power BI. - Provide technical direction across the full model lifecycle: problem framing, data preparation, feature engineering, algorithm selection, training, validation, deployment, monitoring, drift detection, and optimization. - Evaluate and recommend modeling approaches including regression techniques, time-series and forecasting methods, deep learning, optimization algorithms, and advanced statistical approaches. - Lead decisions on compute configuration, GPU acceleration, scalability, deployment patterns, and Azure cost/performance tuning, while staying current on emerging AI and agent technologies. - Directly manage and mentor a Senior Data Scientist, providing oversight on technical quality, delivery execution, client communication, and professional development.

Requirements

- 10+ years of hands-on experience in data science, machine learning, AI, advanced analytics, or a closely related discipline. - Prior experience in a technical architecture, lead, principal, or AI/ML architect role. - Strong Python proficiency for data science, ML, deep learning, statistical modeling, and production-grade development. - Deep experience with Azure data and AI services, including Azure Machine Learning and related Azure data and AI tooling. - Hands-on PyTorch experience for deep learning model development. - Strong foundation in statistics, regression, forecasting, optimization, and machine learning methodology, with practical experience applying methods such as ARIMA, TBATS, Temporal Fusion Transformer, Prophet, or similar. - Demonstrated experience developing, deploying, owning, and monitoring production ML models, including drift detection and performance measurement. - Experience working with structured, semi-structured, and unstructured data on platforms such as data lakes, data warehouses, APIs, and NoSQL stores. - Hands-on coding ability and critical thinking beyond reliance on AutoML or off-the-shelf tools.

Nice to have

- Familiarity with Azure AI Foundry, Azure OpenAI, Azure AI Search, M365 Agents, or comparable AI and agent frameworks. - Domain exposure to manufacturing, distribution, energy, or supply chain analytics. - Consulting or client-facing experience translating ambiguous business problems into structured technical solutions.

Benefits and work setup

- Remote with light travel to client sites as needed; full-time employment. - Unlimited paid time off, 401(k) plan with company matching, and a monthly home office stipend. - Subsidized medical, dental, and vision coverage, plus health savings and flexible spending accounts. - Company-paid life and disability insurance, and support for training, certifications, and continuing education.

Nice to have

- Familiarity with Azure AI Foundry, Azure OpenAI, Azure AI Search, M365 Agents, or comparable AI and agent frameworks. - Domain exposure to manufacturing, distribution, energy, or supply chain analytics. - Consulting or client-facing experience translating ambiguous business problems into structured technical solutions.