MCA Connect
Title
Senior Data Scientist
Location Fully Remote with travel up to 10%
Description The Senior Data Scientist is responsible for being a partner to team members, a mentor, and directly enabling growth of the Manufacturing Intelligence team at MCA. The Sr. Data Scientist is expected to lead Data Science implementations, drive innovation, upskill and mentor junior teammates, and partner with leadership to drive continuous improvement.
Within a project framework, the Sr. Data Scientist is responsible for building client relationships, effectively utilizing Agile project delivery methodologies, implementing scalable and efficient technical architectures to satisfy client requirements, and performing hands‑on implementations of Microsoft data solutions for MCA Connect’s current and future customers/projects.
Responsibilities
Lead end‑to‑end data science and ML engagements on Microsoft Azure, Microsoft Fabric, and Databricks to solve manufacturing, supply chain, and operations use cases for MCA clients.
Design and implement advanced analytics solutions using Azure Machine Learning, Azure OpenAI, Databricks (PySpark/MLflow), and Power BI, ensuring they are secure, scalable, and aligned to MCA delivery standards.
Build modern retrieval and RAG patterns on Azure (vectorization, similarity search) to expose knowledge from SOPs, service histories, maintenance logs, and data sources commonly associated with manufacturing, supply chain, and logistics.
Develop, fine‑tune, and deploy models across the spectrum—from classical and statistical approaches (forecasting, optimization, anomaly detection) to deep learning and Generative AI—selecting the simplest viable approach for the business problem.
Perform hands‑on LLM adaptation (such as LoRA/QLoRA) and package or customize models for production hosting in Azure and Databricks.
Translate client business requirements into technical architectures, feature roadmaps, and implementation plans, and advise when a regression or optimization solution is more appropriate than a complex GenAI approach.
Analyze large, complex, and mixed (structured and unstructured) datasets to generate actionable insights for plant, quality, and supply chain stakeholders.
Present model results, data stories, and recommended actions in a way that non‑technical leaders can understand and operationalize.
Partner with data engineering and architecture teams to improve data capture, data quality, and automation across Azure data lakes, warehouses, and ERP/MES sources.
Create reusable notebooks, components, and delivery patterns to raise the maturity of the Manufacturing Intelligence team and accelerate future projects.
Ensure MLOps best practices for the full model lifecycle, including tracking, registry, CI/CD, monitoring, and governance using Azure ML and Databricks.
Manage and prioritize multiple client projects simultaneously, including requirements gathering, scope definition, timeline coordination, and status reporting.
Contribute to Statements of Work and proposals that clearly define services, effort, and the Azure/Databricks solution approach.
Stay current on Microsoft and Databricks product roadmaps and industry changes and recommend adoption where it improves client outcomes.
Meet with clients to understand problems, validate assumptions, and shape analytics and AI solutions.
Be available for approximately 10 percent travel on an as‑needed basis.
Qualifications
9+ years of hands‑on experience delivering data science and ML/AI solutions on cloud platforms, preferably Microsoft Azure and Databricks; experience with data engineering patterns is a plus.
Degree in a quantitative field such as Statistics, Mathematics, Computer Science, Engineering, AI, or Analytics, or an equivalent combination of education and experience.
Proven ownership of the full MLOps lifecycle—experiment tracking, model registration, deployment, monitoring, and retraining—using Azure ML and Databricks/MLflow, ideally with CI/CD in Azure DevOps or GitHub.
Ability to translate manufacturing, supply chain, or operations business problems into technical requirements by partnering with business stakeholders, data scientists, and data/solution architects.
Strong consulting‑grade communication skills, including storytelling with analytics and visualization, to explain model behavior and drive decisions with non‑technical audiences.
Expert‑level proficiency in Python and SQL, with T‑SQL preferred for Azure SQL/Synapse/Fabric environments.
Deep, hands‑on experience with core data science libraries, including scikit‑learn for classical and statistical ML and PyTorch for deep learning.
Proven experience working in Databricks with PySpark, plus hands‑on use of optimization approaches commonly applied in supply chain and manufacturing—such as mixed‑integer/linear programming for planning and scheduling, network/routing optimization, and constraint‑based solvers—using libraries like OR‑Tools or SciPy optimize.
Practical knowledge of Generative AI on Azure, including prompt engineering, retrieval‑augmented generation (RAG), and the ability to identify and prioritise high‑value LLM use cases in a business context.
Hands‑on experience implementing RAG and vectorisation on Microsoft‑first tooling (for example Azure AI Search/Vector Search, Azure OpenAI, Databricks vector stores or Lakehouse‑based retrieval pipelines), including document ingestion, chunking, and securing access to manufacturing and supply chain content.
Hands‑on experience building and orchestrating AI agents in the Microsoft ecosystem (Azure AI Studio/Agents, Azure OpenAI with tool/function calling, and integrations to Databricks/Fabric or REST backends), including securing those agents against tenant data and conditioning them for manufacturing and supply chain workflows.
Extensive experience integrating with Azure‑centric data platforms, including data lakes, data warehouses, Delta/Parquet, NoSQL stores, and APIs.
Ability to stand up and manage data and experimentation environments to support rapid model development and evaluation.
Strong critical thinking, active listening, and situational awareness to infer business needs, understand context, and turn loose direction into concrete analytical solutions.
Attention to detail and a bias for end‑to‑end ownership of deliverables in a consulting/project setting.
Experience delivering projects on large, complex datasets, preferably in manufacturing, supply chain, or logistics industries.
Demonstrated ability to communicate effectively with technical teams, business stakeholders, and client leadership.
Salary $105,000 - $165,000 a year
Bonus PLUS
Uncapped Supplemental Compensation (Bonus) Plan paid out quarterly
Benefits
Work/Life Balance with Unlimited Paid Time Off (UPTO)
401k Plan with Company Matching Contribution
Monthly Stipend for Home Office Expenses
Subsidised Medical, Dental and Vision Coverage
Health Savings and Flexible Spending Accounts
Company Paid Life and Disability Insurance
Training, Certification and Continuing Education Support
Why work for MCA Connect? MCA Connect offers limitless opportunities for personal and professional growth in a stimulating, challenging, and performance‑oriented work culture where you can share your ideas and make impactful daily contributions. Our employees are highly motivated and talented individuals dedicated to developing, marketing, and selling products designed to deliver value for mid‑market and enterprise‑size manufacturing, distribution, and energy companies. We take the time to train our consultants so that they understand the industries we serve and can deliver best practices, proven methodologies, and ongoing industry expertise to our clients.
MCA Connect is an Equal Opportunity Employer. MCA Connect promotes equal employment opportunity to all employees and applicants and does not discriminate on the basis of race, religion, color, creed, national origin, sex, age, sexual/gender orientation, status as a protected disabled or Vietnam Era Veteran, disability, or any other legally protected status. We firmly believe our differences make us stronger!
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Location Fully Remote with travel up to 10%
Description The Senior Data Scientist is responsible for being a partner to team members, a mentor, and directly enabling growth of the Manufacturing Intelligence team at MCA. The Sr. Data Scientist is expected to lead Data Science implementations, drive innovation, upskill and mentor junior teammates, and partner with leadership to drive continuous improvement.
Within a project framework, the Sr. Data Scientist is responsible for building client relationships, effectively utilizing Agile project delivery methodologies, implementing scalable and efficient technical architectures to satisfy client requirements, and performing hands‑on implementations of Microsoft data solutions for MCA Connect’s current and future customers/projects.
Responsibilities
Lead end‑to‑end data science and ML engagements on Microsoft Azure, Microsoft Fabric, and Databricks to solve manufacturing, supply chain, and operations use cases for MCA clients.
Design and implement advanced analytics solutions using Azure Machine Learning, Azure OpenAI, Databricks (PySpark/MLflow), and Power BI, ensuring they are secure, scalable, and aligned to MCA delivery standards.
Build modern retrieval and RAG patterns on Azure (vectorization, similarity search) to expose knowledge from SOPs, service histories, maintenance logs, and data sources commonly associated with manufacturing, supply chain, and logistics.
Develop, fine‑tune, and deploy models across the spectrum—from classical and statistical approaches (forecasting, optimization, anomaly detection) to deep learning and Generative AI—selecting the simplest viable approach for the business problem.
Perform hands‑on LLM adaptation (such as LoRA/QLoRA) and package or customize models for production hosting in Azure and Databricks.
Translate client business requirements into technical architectures, feature roadmaps, and implementation plans, and advise when a regression or optimization solution is more appropriate than a complex GenAI approach.
Analyze large, complex, and mixed (structured and unstructured) datasets to generate actionable insights for plant, quality, and supply chain stakeholders.
Present model results, data stories, and recommended actions in a way that non‑technical leaders can understand and operationalize.
Partner with data engineering and architecture teams to improve data capture, data quality, and automation across Azure data lakes, warehouses, and ERP/MES sources.
Create reusable notebooks, components, and delivery patterns to raise the maturity of the Manufacturing Intelligence team and accelerate future projects.
Ensure MLOps best practices for the full model lifecycle, including tracking, registry, CI/CD, monitoring, and governance using Azure ML and Databricks.
Manage and prioritize multiple client projects simultaneously, including requirements gathering, scope definition, timeline coordination, and status reporting.
Contribute to Statements of Work and proposals that clearly define services, effort, and the Azure/Databricks solution approach.
Stay current on Microsoft and Databricks product roadmaps and industry changes and recommend adoption where it improves client outcomes.
Meet with clients to understand problems, validate assumptions, and shape analytics and AI solutions.
Be available for approximately 10 percent travel on an as‑needed basis.
Qualifications
9+ years of hands‑on experience delivering data science and ML/AI solutions on cloud platforms, preferably Microsoft Azure and Databricks; experience with data engineering patterns is a plus.
Degree in a quantitative field such as Statistics, Mathematics, Computer Science, Engineering, AI, or Analytics, or an equivalent combination of education and experience.
Proven ownership of the full MLOps lifecycle—experiment tracking, model registration, deployment, monitoring, and retraining—using Azure ML and Databricks/MLflow, ideally with CI/CD in Azure DevOps or GitHub.
Ability to translate manufacturing, supply chain, or operations business problems into technical requirements by partnering with business stakeholders, data scientists, and data/solution architects.
Strong consulting‑grade communication skills, including storytelling with analytics and visualization, to explain model behavior and drive decisions with non‑technical audiences.
Expert‑level proficiency in Python and SQL, with T‑SQL preferred for Azure SQL/Synapse/Fabric environments.
Deep, hands‑on experience with core data science libraries, including scikit‑learn for classical and statistical ML and PyTorch for deep learning.
Proven experience working in Databricks with PySpark, plus hands‑on use of optimization approaches commonly applied in supply chain and manufacturing—such as mixed‑integer/linear programming for planning and scheduling, network/routing optimization, and constraint‑based solvers—using libraries like OR‑Tools or SciPy optimize.
Practical knowledge of Generative AI on Azure, including prompt engineering, retrieval‑augmented generation (RAG), and the ability to identify and prioritise high‑value LLM use cases in a business context.
Hands‑on experience implementing RAG and vectorisation on Microsoft‑first tooling (for example Azure AI Search/Vector Search, Azure OpenAI, Databricks vector stores or Lakehouse‑based retrieval pipelines), including document ingestion, chunking, and securing access to manufacturing and supply chain content.
Hands‑on experience building and orchestrating AI agents in the Microsoft ecosystem (Azure AI Studio/Agents, Azure OpenAI with tool/function calling, and integrations to Databricks/Fabric or REST backends), including securing those agents against tenant data and conditioning them for manufacturing and supply chain workflows.
Extensive experience integrating with Azure‑centric data platforms, including data lakes, data warehouses, Delta/Parquet, NoSQL stores, and APIs.
Ability to stand up and manage data and experimentation environments to support rapid model development and evaluation.
Strong critical thinking, active listening, and situational awareness to infer business needs, understand context, and turn loose direction into concrete analytical solutions.
Attention to detail and a bias for end‑to‑end ownership of deliverables in a consulting/project setting.
Experience delivering projects on large, complex datasets, preferably in manufacturing, supply chain, or logistics industries.
Demonstrated ability to communicate effectively with technical teams, business stakeholders, and client leadership.
Salary $105,000 - $165,000 a year
Bonus PLUS
Uncapped Supplemental Compensation (Bonus) Plan paid out quarterly
Benefits
Work/Life Balance with Unlimited Paid Time Off (UPTO)
401k Plan with Company Matching Contribution
Monthly Stipend for Home Office Expenses
Subsidised Medical, Dental and Vision Coverage
Health Savings and Flexible Spending Accounts
Company Paid Life and Disability Insurance
Training, Certification and Continuing Education Support
Why work for MCA Connect? MCA Connect offers limitless opportunities for personal and professional growth in a stimulating, challenging, and performance‑oriented work culture where you can share your ideas and make impactful daily contributions. Our employees are highly motivated and talented individuals dedicated to developing, marketing, and selling products designed to deliver value for mid‑market and enterprise‑size manufacturing, distribution, and energy companies. We take the time to train our consultants so that they understand the industries we serve and can deliver best practices, proven methodologies, and ongoing industry expertise to our clients.
MCA Connect is an Equal Opportunity Employer. MCA Connect promotes equal employment opportunity to all employees and applicants and does not discriminate on the basis of race, religion, color, creed, national origin, sex, age, sexual/gender orientation, status as a protected disabled or Vietnam Era Veteran, disability, or any other legally protected status. We firmly believe our differences make us stronger!
#J-18808-Ljbffr