Lead Data Scientist - Solution Architect
Confirmed on the employer's own hiring board on Sep 26, 2026. First seen by Alion on Sep 25, 2026.
Lead Data Scientist - Solution Architect
Location
Remote with light travel as needed to client sites
Employment Type
Full-time
Position Summary
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.
Required Qualifications
10+ years of hands-on experience in data science, machine learning, AI, advanced analytics, or related technical disciplines.
Prior experience in technical architecture, lead data scientist, principal data scientist, AI/ML architect, or similar senior-level role.
Strong proficiency in Python for data science, machine learning, deep learning, statistical modeling, and production-level solutions.
Experience with Spark and large-scale data processing.
Strong experience with Azure-based data and AI technologies, including Azure Machine Learning and related Azure data services.
Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, M365 Agents, or similar AI/agent frameworks.
Hands-on experience with PyTorch for deep learning model development.
Strong foundation in statistics, regression, forecasting, optimization, and machine learning methodology.
Experience developing, deploying, owning, and monitoring production-level machine learning models.
Ability to evaluate and apply time-series and forecasting techniques such as ARIMA, TBATS, Temporal Fusion Transformer, Prophet, or similar methods.
Experience working with structured, semi-structured, and unstructured data.
Experience connecting to and working with data platforms such as data lakes, data warehouses, APIs, NoSQL databases, and cloud-native data services.
Ability to translate business needs into technical requirements through active partnership with clients, stakeholders, data scientists, data engineers, and data architects.
Strong communication and storytelling skills, with the ability to explain technical concepts and model outputs to non-technical audiences.
Demonstrated ability to lead complex client-facing engagements and manage multiple priorities.
Strong problem-solving mindset with curiosity, perseverance, and the ability to work through incomplete systems or ambiguous data challenges.
Experience mentoring, coaching, or managing technical team members.
Must be open to approximately 10% travel as needed.
Preferred Qualifications
Master’s or Ph.D. in Computer Science, Statistics, Applied Mathematics, Data Science, Engineering, Operations Research, or a related field.
Strong academic foundation in statistics, computer science, mathematics, optimization, or research-based analytical methods.
Published research, thesis work, National Academy of Sciences affiliation, or other demonstrated research depth.
Experience in manufacturing, supply chain, demand forecasting, inventory optimization, quality analytics, production analytics, or industrial operations.
Experience with cloud-native or ML-native organizations, research-heavy environments, or algorithmic optimization-focused teams.
Experience with C++, GPU acceleration, distributed training, or compute optimization.
Experience configuring Azure compute environments for machine learning performance, scalability, and cost efficiency.
Experience with Databricks, Azure Databricks, or equivalent big data and ML engineering platforms.
Experience supporting proposal development, solution estimation, technical sales support, or pre-sales activities.
Prior consulting experience in a client-facing technical leadership role.
Ideal Candidate Profile
The ideal candidate is a hands-on technical architect who can operate at both the strategic and execution level. This person should be able to sit with a client to understand a business problem, evaluate the available data, define the technical path forward, guide the modeling and architecture approach, and mentor a Senior Data Scientist Consultant through successful delivery.
They should bring strong academic or practical depth in data science and machine learning, but also the communication skills and consulting mindset needed to work directly with manufacturing and supply chain clients. They should be comfortable with ambiguity, curious enough to investigate messy data environments, and experienced enough to know when to escalate, simplify, optimize, or re-architect a solution.
This role is best suited for someone who is not solely reliant on AutoML or out-of-the-box tools, but instead has the hands-on coding ability, statistical foundation, and architectural judgment to build scalable, client-ready AI and machine learning solutions.