Data Scientist

$60K - $107K Minnetonka, MN, US Mid Level Data Scientist

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Skills & Technologies

AnthropicAwsAzureGcpHugging FaceKubernetesLangchainLlamaLlamaindexMlflow

About This Role

AI job market dashboard showing open roles by category

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best.Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale.Join us to start Caring. Connecting. Growing together.

We are the UHC Payment Integrity AI/ML Engineering Team, responsible for designing and deploying advanced Machine Learning and Generative AI solutions that prevent Fraud, Waste, and Abuse (FWA) across the healthcare claims ecosystem.

Our work spans pre‑payment prediction, post‑payment anomaly detection, intelligent automation, and next‑generation LLM‑based decisioning.

The team is a blend of AI/ML engineers, data scientists, data engineers, and GenAI specialists who collaborate to build and scale end‑to‑end ML systems. We align closely with the organization's mission of improving affordability, operational efficiency, and trust in healthcare data

You will enjoy the flexibility to telecommute\* from anywhere within the U.S. as you take on some tough challenges.

Primary Responsibilities:

  • Design, train, finetune, and deploy Large Language Models (LLMs) and Generative AI components for claims automation, anomaly detection, and investigative workflows
  • Build and operationalize ML pipelines using Python, PySpark, and cloud\-native architectures (Azure/AWS/GCP)
  • Develop traditional machine learning models (classification, anomaly detection, NLP pipelines) for high‑volume healthcare datasets
  • Implement RAG (Retrieval‑Augmented Generation) systems, embedding models, and vector database integrations
  • Develop automated data processing, feature engineering, and model training pipelines using Spark, MLflow, Databricks, and big‑data ecosystems
  • Partner with product, engineering, and clinical domain teams to translate complex business challenges into scalable ML and GenAI solutions
  • Optimize and monitor ML models in production, ensuring accuracy, latency, compliance, and responsible‑AI best practices
  • Present AI/ML solution designs, model insights, and GenAI architecture recommendations to technical and non‑technical stakeholders
  • Design, develop, and deploy AI\-powered solutions to address complex business challenges with emphasis on responsible use of AI

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 CS or IT related field
  • 5\+ years of hands‑on experience in AI/ML engineering, deep learning, or applied machine learning
  • 3\+ years of experience in Python, PySpark, ML frameworks (TensorFlow, PyTorch), and distributed training
  • 3\+ years of experience with big‑data systems (like Hadoop, Spark, Hive) and cloud platforms (like Azure, AWS, GCP)
  • 2\+ years of experience with LLMs, including:

+ Finetuning (LoRA, QLoRA, PEFT, SFT, or RLHF)

+ Prompt engineering \& system design

+ RAG pipelines \& vector search

Preferred Qualifications:

  • Prior experience with US healthcare datasets (claims, clinical, EMR/EHR, provider networks, payer ops)
  • Experience deploying ML/LLM workloads using Databricks, MLflow, Kubernetes, or serverless inference
  • Familiarity with modern GenAI tooling (LangChain, LlamaIndex, HuggingFace, OpenAI/Anthropic/Azure‑OpenAI APIs)
  • Knowledge of deep learning architectures (Transformers, sequence models, contrastive learning)
  • Experience optimizing model inference using quantization, distillation, or distributed GPU compute
  • Demonstrated success in AI product delivery, cross‑functional collaboration, and influencing technical strategy
  • Strong grounding in ML fundamentals (feature engineering, model evaluation, A/B testing, MLOps best practices)
  • All Telecommuters 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 rangefrom $60,200 to $107,400 annually based on full\-time employment. We comply with all minimum wage laws as applicable.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

*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.*

\#RPO \#GREEN

Salary Context

This $60K-$107K range is in the lower quartile for Data Scientist roles in our dataset (median: $166K across 345 roles with salary data).

View full Data Scientist salary data →

Role Details

Company Optum
Title Data Scientist
Location Minnetonka, MN, US
Category Data Scientist
Experience Mid Level
Salary $60K - $107K
Remote No

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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Optum, 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

Anthropic (3% of roles) Aws (34% of roles) Azure (10% of roles) Gcp (9% of roles) Hugging Face (2% of roles) Kubernetes (4% of roles) Langchain (4% of roles) Llama (2% of roles) Llamaindex (1% of roles) Mlflow (1% 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 $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($83K) sits 59% below the category median. Disclosed range: $60K to $107K.

Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.

Optum AI Hiring

Optum has 23 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist, MLOps Engineer. Positions span Sheffield, OH, US, Eden Prairie, MN, US, Hartford, CT, US. Compensation range: $74K - $273K.

Location Context

Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).

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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.

The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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 441 roles with disclosed compensation, the median salary for Data Scientist positions is $204,700. 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 7% of the 26,159 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.
Optum 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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