Sr Data Scientist - AI or ML Engineering Focus - Remote

$91K - $163K Remote Senior Data Scientist

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Skills & Technologies

MlflowPython

About This Role

AI job market dashboard showing open roles by category

At UnitedHealthcare, we're simplifying the health care experience, creating healthier communities and removing barriers to quality care. The work you do here impacts the lives of millions of people for the better. Come build the health care system of tomorrow, making it more responsive, affordable and optimized. Ready to make a difference? Join us to start Caring. Connecting. Growing together

A Senior Data Scientist (Grade 28\) specializing in AI/ML engineering for EAR projects impacting the UHC COVE. This role is a primary hands\-on contributor for building and deploying machine learning models. It emphasizes practical engineering of AI solutions: rigorous model development, evaluation of modeling approaches, and implementing MLOps pipelines to ensure models are effectively integrated and maintained in production.

You'll enjoy the flexibility to work remotely \* from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Primary Responsibilities:

  • Model Development \& Evaluation: Develop machine learning models tailored to project requirements (classification, regression, forecasting, etc.). Perform thorough evaluations and experiments to compare model performance and iterate to optimize results. Ensure that chosen models are robust and generalize well
  • MLOps \& Deployment: Take ownership of deploying models into production. Build and maintain automated pipelines for model serving, including steps for data preprocessing, model inference, and continuous monitoring. Implement version control for models and manage a schedule for retraining or refreshing models as data evolves
  • Solution Engineering: Integrate AI/ML solutions with the broader application ecosystem. Work closely with data engineers to ensure pipelines provide the needed data to models in the correct format and frequency (batch or streaming). Collaborate with software engineers to embed model outputs into user\-facing applications or workflows
  • Technical Innovation: Stay up to date with new tools and techniques in AI engineering. Evaluate external solutions or libraries that could accelerate development (e.g., AutoML tools, specialized model frameworks) and make recommendations. Build proof\-of\-concept demonstrations to assess new approaches or technologies for the project
  • Operational Excellence: Implement monitoring and alerting for model performance and data drift. Troubleshoot issues in model predictions or system integration quickly. Optimize model runtime and resource usage (e.g., improving inference speed or reducing memory footprint) to meet production SLAs

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

Required Qualifications:

  • Bachelor's Degree in Computer Science, Data Science, or related field
  • Solid Data Science Background: 5\+ years of experience in data science, machine learning, or related roles. Solid foundation in statistical modeling and machine learning techniques. Hands\-on experience developing models for real\-world problems and improving them based on feedback and data
  • Production ML Experience: Demonstrated experience deploying and maintaining ML models in a production environment. Comfort with the end\-to\-end MLOps lifecycle: using source control, CI/CD pipelines, and orchestration to automate model deployment. Should understand concepts like model versioning, reproducibility, and monitoring in production
  • Programming \& Data Skills: Proficiency in Python and PySpark for building ML models and automating tasks. Solid SQL skills for data extraction and manipulation
  • Problem\-Solving \& Autonomy: Proven ability to work independently on complex technical problems. Solid troubleshooting skills to debug issues whether they stem from data quality, model behavior, or pipeline failures. A mindset geared towards automation and efficiency, always looking for ways to streamline repetitive tasks

Preferred Qualifications:

  • MLOps Tooling: Experience with specific MLOps and cloud tools (e.g., Databricks MLflow for experiment tracking and model registry, GitHub Actions for CI). Familiarity with infrastructure\-as\-code for deploying ML infrastructure
  • Leadership \& Collaboration: Experience in mentoring junior data scientists or leading technical workstreams will be beneficial. The ability to document work clearly and impart knowledge to others helps the overall team
  • Databricks \& Spark: Familiarity with the Databricks Lakehouse platform and Spark. For example, knowing how to implement ML pipelines on Databricks, use Delta Lake for data versioning, and optimize Spark jobs for feature processing
  • Real\-Time Systems: Exposure to real\-time or streaming data analysis. Experience deploying models that consume streaming data (e.g., streaming analytics or real\-time dashboards) or working with technologies like Kafka for live data feeds
  • All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy

Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far\-reaching choice of benefits and incentives. The salary for this role will range from $91,700 \- $163,700 annually based on full\-time employment. We comply with all minimum wage laws as applicable.

Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.

*At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone\-of every race, gender, sexuality, age, location and income\-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes \- an enterprise priority reflected in our mission.*

*UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.*

*UnitedHealth Group is a drug\-free workplace. Candidates are required to pass a drug test before beginning employment.*

Salary Context

This $91K-$163K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).

View full Data Scientist salary data →

Role Details

Title Sr Data Scientist - AI or ML Engineering Focus - Remote
Location Minnetonka, MN, US
Category Data Scientist
Experience Senior
Salary $91K - $163K
Remote Yes

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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At UnitedHealthcare, 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

Mlflow (4% of roles) Python (52% of roles)

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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($127K) sits 34% below the category median. Disclosed range: $91K to $163K.

Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.

UnitedHealthcare AI Hiring

UnitedHealthcare has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Cypress, CA, US, Minnetonka, MN, US. Compensation range: $163K - $579K.

Remote Work Context

Remote AI roles pay a median of $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.

The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.

Frequently Asked Questions

Based on 789 roles with disclosed compensation, the median salary for Data Scientist positions is $192,890. Actual compensation varies by seniority, location, and company stage.
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.
About 15% of the 4,317 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
UnitedHealthcare is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from Data Scientist positions include Senior Data Scientist, ML Engineer, AI Product Manager. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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