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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. Title Senior Data Scientist
Location Virtual, home based office with travel
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
- Leveraging advanced analytics techniques and tools such as Azure Machine Learning, Azure Open AI, Azure AI Search, Azure Synapse Analytics, Databricks, and Power BI
- Extract insights from large datasets and develop data\-driven solutions.
- Lead end\-to\-end data science projects, collaborate with cross\-functional teams, and stay updated with emerging Microsoft technologies.
- Strong problem\-solving, programming, and communication skills.
- Building predictive models and other analytical solutions.
- Apply statistical and machine learning methods to analyze large, complex data sets.
- Collaborate with various parties to brainstorm creative uses for data.
- Develop complex algorithms and statistical predictive models and determine analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes.
- Perform analyses of structured and unstructured data to solve multiple and complex business problems.
- Deliver actionable guidance and recommendations to stakeholders to help understand nuances of the models built and tactics for improving.
- Act as the subject matter expert on solutions produced by working closely with your peers to help Clients better understand their data.
- Actively participate in the execution and planning of data capture strategies to improve data automation, data quality, and analytics capabilities.
- Run ad\-hoc data extracts for requests from diverse partners and automate those needs over time.
- Perform highly complex, technical, and creative predictive analytics projects.
- Manage and prioritize multiple moderate\-to\-highly complex projects including gathering business requirements, developing project goals and requirements, coordinating project timelines, and communicating project status and deliverables with Clients.
- Stay abreast of architectural/industry changes in related fields.
- Continuously improve Team processes to ensure information is of the highest quality, contributing to the overall effectiveness of the Team.
- Ensure best practices are followed and business objectives are achieved by focusing on process improvements.
- Meet with Clients to determine needs and/or identify problem areas.
- May be responsible for the development of Statement of Work and Proposals which outline professional services required to meet Client requirements.
- Must be open to \~10% travel on an as needed basis.
### Qualifications
- 7\+ years of hands\-on experience with data science, AI, and big data. Experience with data engineering is a plus.
- Degree in Information Technology or Business Administration or equivalent combination of education and experience
- Experience developing and owning production\-level models using various data sets.
- Experience shipping models to production, and ensuring continuous availability via measurements, and tools like statistical process control.
- Ability to translate business needs into technical requirements through active partnerships, collaborating with partners, data scientists and data architects.
- Compelling storytelling through analytics and visualization, getting your point across, and keeping your audience engaged.
- Experience working in Python, R, Scala, Spark, and/or T\-SQL.
- Extensive experience connecting to Data Platforms including data lakes, data warehouses, NoSQL databases, and APIs.
- Ability to set up data and experimental platforms.
- Strong critical thinking, active listening, and communication skills to infer business needs, grasp the underlying context, and translate loose direction on analytical projects into concrete solutions.
- Attention to detail and desire for end\-to\-end ownership of deliverables.
- Experience working on projects involving large volumes of data, preferably in the Manufacturing industry.
- Demonstrates the ability to communicate effectively with technical teams, business stakeholders, and other relevant parties
PLUS Supplemental Compensation (Bonus) Plan paid out 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. 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 $105K-$165K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 32% below the category median. Disclosed range: $105K to $165K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
MCAConnect AI Hiring
MCAConnect has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $165K - $165K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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