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About This Role
Through passion and deep industry expertise, MCA Connect helps manufacturers succeed by unlocking innovation with actionable business insights. Our strategic solutions, innovation, and industry intelligence help manufacturers gain visibility, improve profitability, and achieve a competitive edge.
Established in 2002, MCA Connect has grown into one of the largest US-based solution partners in Microsoft Business Applications and Azure Data & AI / Digital & App Innovation. Our Microsoft Specialties include Finance and Supply Chain, Analytics on Azure, Data Warehouse Migration, and Power Platform. We’re also a fifteen-time Microsoft Partner of the Year and three-time Inc. Best Workplaces award winner.
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.
Plus additional supplemental compensation (bonus) paid quarterlyWhy work for MCA Connect? Our compensation plan offers one of the best bonus structures in the industry. Along with this we also offer a generous benefit package:* Work/Life Balance with Unlimited Paid Time Off (UPTO)
- 401k Plan with Company Matching Contribution
- Monthly Stipend for Home Office Expenses
- Subsidized Medical, Dental and Vision Coverage
- Health Savings and Flexible Spending Accounts
- Company Paid Life and Disability Insurance
- Training, Certification and Continuing Education Support
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! We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $180K-$240K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 172 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At MCAConnect, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($210K) sits 11% above the category median. Disclosed range: $180K to $240K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
MCAConnect AI Hiring
MCAConnect has 2 open AI roles right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $210K - $240K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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