Lead Data Scientist - Solution Architect

The Azure Data Science Architect is responsible for providing technical leadership, architectural direction, and hands-on guidance across complex data science, AI, machine learning, and advanced analytics initiatives for MCA Connect clients. This role will serve as a senior technical advisor and solution owner, helping clients translate business problems into scalable, production-ready AI and data science solutions.

In addition to individual technical leadership, this role will include a people management component. The Azure Data Science Architect will directly manage and mentor a Senior Data Scientist, providing oversight on technical quality, delivery execution, client communication, professional development, and alignment to MCA’s standards and best practices.

The ideal candidate will bring deep expertise in machine learning and AI development, Azure data and AI services, statistical modeling, forecasting, optimization, and production model deployment. This person should be comfortable engaging directly with customers, working through messy or incomplete data environments, providing architectural recommendations, and leading both technical and non-technical stakeholders through complex analytical solutions.

Key Responsibilities

Solution Architecture & Technical Leadership

• Serve as the architectural lead for complex data science, AI, machine learning, forecasting, optimization, and advanced analytics engagements.
• Partner with clients to understand business challenges, gather requirements, identify data limitations, and translate business needs into scalable technical solutions.
• Design and guide the implementation of production-ready data science and AI solutions using Azure Machine Learning, Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Synapse Analytics, Databricks, Spark, Power BI, and related Microsoft technologies.
• Provide technical direction on model design, algorithm selection, data preparation, feature engineering, training, validation, deployment, monitoring, and optimization.
• Evaluate and recommend appropriate modeling approaches, including regression techniques, forecasting models, deep learning methods, optimization algorithms, and advanced statistical approaches.
• Lead architecture decisions related to compute configuration, GPU acceleration, model performance, scalability, deployment patterns, and Azure cost/performance optimization.
• Ensure solutions are designed for long-term maintainability, scalability, observability, and business value.
• Stay current with emerging Microsoft data, AI, and agent technologies, including Azure AI Foundry, M365 Agents, Azure OpenAI, and related tools.
• Act as a subject matter expert for internal teams and clients on data science architecture, AI strategy, machine learning engineering, and advanced analytics delivery.

Delivery & Client Engagement

• Lead client-facing discovery and requirement gathering sessions to define project goals, business outcomes, technical requirements, and success measures.
• Work directly with customers to understand business processes, analytical needs, data maturity, and operational constraints.
• Guide project teams through ambiguous, incomplete, or messy data environments by diagnosing issues, proposing solutions, and escalating appropriately when needed.
• Communicate complex analytical and technical concepts clearly to both technical teams and business stakeholders.
• Deliver actionable recommendations that help clients understand model outputs, business implications, risks, limitations, and opportunities for improvement.
• Support the development of Statements of Work, proposals, solution estimates, technical approach documentation, and project plans as needed.
• Collaborate with data engineers, data architects, project managers, business analysts, and client stakeholders to ensure successful end-to-end delivery.
• Ensure data science solutions align to client goals, MCA delivery standards, Microsoft best practices, and long-term supportability.

People Management & Mentorship

• Directly manage, mentor, and support a Senior Data Scientist Consultant.
• Provide regular coaching, feedback, and technical guidance to support professional growth and project success.
• Review technical deliverables, model design decisions, code quality, documentation, and client-facing outputs.
• Help prioritize work, remove blockers, and ensure the Senior Data Scientist Consultant is aligned to project goals and client expectations.
• Support performance management, goal setting, skills development, and career growth for direct report(s).
• Foster a collaborative, curious, and high-accountability team culture.
• Partner with Data & AI leadership to identify opportunities for team improvement, knowledge sharing, reusable assets, and delivery process enhancements.

Data Science, AI & Machine Learning Expertise

• Build, review, and guide the development of predictive models, statistical models, optimization models, forecasting solutions, and other analytical applications.
• Apply advanced statistical and machine learning methods to large, complex structured and unstructured datasets.
• Use Python as the primary programming language for model development, data exploration, experimentation, and production-ready analytical solutions.
• Work with Spark and large-scale data processing frameworks to support high-volume analytics and machine learning workloads.
• Develop and evaluate deep learning models using PyTorch.
• Apply and explain multiple regression techniques, time-series approaches, and forecasting models.
• Work with algorithms and methods such as ARIMA, TBATS, Temporal Fusion Transformer, Prophet, and other relevant forecasting or optimization techniques.
• Support production model deployment, monitoring, drift detection, availability, and performance measurement.
• Lead experimentation and model validation processes to ensure solutions are accurate, explainable, and aligned with business outcomes.
• Avoid over-reliance on AutoML by demonstrating hands-on coding ability, critical thinking, and strong foundational understanding of machine learning and statistical methods.