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About This Role
Your Future is our Future
At Lumeris, we believe that our greatest achievements are made possible by the talent and commitment of our team members. That's why we are actively seeking talented and collaborative individuals who are passionate about making a difference in the healthcare industry. Join us today as we strive to create a system of care that every doctor wants for their own family and become part of a community that values its people and empowers you to make an impact.
We're excited to consider every qualified candidate authorized to work in the United States, although we are unable to sponsor visas for this role at this time.
Position:
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Sr. Data Scientist \- AI VoicePosition Summary:
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We're seeking a Senior Data Scientist to build the voice AI and model capabilities behind Tom, our AI\-enabled primary care platform. Reporting directly to our VP of AI, you'll split your time between two halves of the same problem: developing and fine\-tuning the models that power clinical AI, and building the voice systems that let us test, stress, and trust those models before they ever reach a patient. This is a hands\-on, build\-first role for someone who has personally trained models and shipped conversational voice systems into production.Job Description:
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Primary Responsibilities
Voice AI Development
- Build and deploy real\-time conversational voice agents, working across the speech stack — speech\-to\-text, LLM reasoning, and text\-to\-speech, as well as newer speech\-to\-speech approaches.
- Solve the practical problems that determine whether voice works in the real world: telephony audio fidelity, latency, interruptions and barge\-in, background noise, and variable capture conditions.
- Develop simulation capabilities that generate and role\-play patient conversations at scale, covering the range of real\-world conditions and edge cases that manual testing cannot reach.
- Make and defend architecture tradeoffs across the voice stack, balancing control, latency, and simplicity.
Model Development \& Fine\-Tuning
- Design and train deep neural networks from the ground up, moving beyond off\-the\-shelf and regression\-based approaches.
- Fine\-tune language models on clinical and longitudinal healthcare data for prediction and generation tasks.
- Select and apply the right training approach for the problem, and articulate clearly where training, fine\-tuning, and prompting each belong.
- Build the data pipelines and transformations that make clinical data usable for model development.
Evaluation \& Safety
- Build evaluation pipelines that measure model output for accuracy, consistency, completeness, and omission — including LLM\-as\-judge approaches.
- Red\-team AI agents to surface failure modes before they reach production, and translate findings into measurable quality improvements.
- Establish the testing discipline and quality signals that clinical reviewers rely on.
Cross\-Functional Partnership
- Partner with clinical, product, and engineering teams to turn ambiguous problems into defined technical work.
- Communicate complex technical concepts credibly to both technical and non\-technical audiences, including company leadership.
- Mentor junior team members and raise the technical standard across the group.
Qualifications
- Bachelor's degree with quantitative major (e.g. statistics, mathematics, economics, and actuarial science) or equivalent.
- 5\+ year of relevant data science, machine learning, or applied AI experience, or the knowledge, skills, and abilities to succeed in the role.
- Must\-have hands\-on experience building and deploying production voice or conversational AI systems — speech and speech\-to\-speech, working with real audio in live environments, not text\-only NLP or chat.
- Must\-have demonstrated experience training neural networks from scratch and fine\-tuning language models, with the ability to explain different training approaches and when each applies.
- Proven track record of shipping, scaling, and hardening AI systems in production.
- Strong programming skills in Python and fluency with modern deep learning frameworks (PyTorch, TensorFlow).
- Working knowledge of real\-time audio infrastructure and telephony integration.
Preferred
- Master's or PhD in a quantitative or scientific discipline.
- Google Cloud Platform (GCP) experience.
- Healthcare or other regulated\-domain experience, including familiarity with clinical data standards such as FHIR.
- Experience building voice models or conversational agents at an AI lab or voice\-first platform.
Working Conditions
- While performing the duties of this job, the employee works in normal office working conditions.
\#LI\-REMOTE
Disclaimer
- The job description describes the general nature and level of work being performed by people assigned to this job and is not intended to be an exhaustive list of all responsibilities, duties and skills required. The physical activities, demands and working conditions represent those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential job duties and responsibilities.
Pay Transparency:
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Factors that may be used to determine your actual pay rate include your specific skills, experience, qualifications, location, and comparison to other employees already in this role. In addition to the base salary, certain roles may qualify for a performance\-based incentive and/or equity, with eligibility depending on the position. These rewards are based on a combination of company performance and individual achievements.
The hiring range for this position is:
$113,800\.00\-$154,525\.00
Benefits of working at Lumeris
- Medical, Vision and Dental Plans
- Tax\-Advantage Savings Accounts (FSA \& HSA)
- Life Insurance and Disability Insurance
- Paid Time Off (PTO, Sick Time, Paid Leave, Volunteer \& Wellness Days)
- Employee Assistance Program
- 401k with company match
- Employee Resource Groups
- Employee Discount Program
- Learning and Development Opportunities
- And much more...
Be part of a team that is changing healthcare!
Member Facing Position:
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Drug Screen Requirement:
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Location:
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MassachusettsTime Type:
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Full timeLumeris and its partners are committed to protecting our high\-risk members \& prospects when conducting business in\-person. All personnel who interact with at\-risk members or prospects are required to have completed, at a minimum, the initial series of an approved COVID\-19 vaccine. If this role has been identified as member\-facing, proof of vaccination will be required as a condition of employment.
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Disclaimer:
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- The job description describes the general nature and level of work being performed by people assigned to this job and is not intended to be an exhaustive list of all responsibilities, duties and skills required. The physical activities, demands and working conditions represent those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individual with disabilities to perform the essential job duties and responsibilities.
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Lumeris is an EEO/AA employer M/F/V/D.
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Salary Context
This $113K-$154K 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
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 Lumeris, 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 $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 ($134K) sits 30% below the category median. Disclosed range: $113K to $154K.
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.
Lumeris AI Hiring
Lumeris has 1 open AI role right now. They're hiring across Data Scientist. Based in MA, US. Compensation range: $154K - $154K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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
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