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About This Role
### About Foodsmart:
Foodsmart is the leading Foodcare platform in the U.S., built to deliver nutrition\-driven healthcare at scale. Powered by a national network of Registered Dietitians, Foodsmart combines personalized clinical nutrition care, behavior change tools, and food benefits to improve member health outcomes while lowering healthcare costs.
Our platform is designed to foster healthier food choices, drive lasting behavior change, and deliver sustainable health outcomes. Through our highly personalized, digital platform, we guide more than 3 million members—including those in employer\-sponsored health plans, regional and national Medicaid managed care organizations, Medicare Advantage plans, and commercial insurers—on a tailored journey to eating well while saving time and money.
Foodsmart seamlessly integrates dietary assessments and nutrition counseling with online food ordering and cost\-effective meal planning for the entire family, optimizing ingredients both at home and on the go. We partner with national and regional retailers across the U.S., many of whom accept SNAP/EBT, making healthier food more accessible. Additionally, we assist members with SNAP enrollment and management, providing tangible access to nutritious food. In 2024, Foodsmart secured a $200 million investment from TPG’s Rise Fund, which supports entrepreneurs dedicated to achieving the United Nations’ Sustainable Development Goals. This investment helps us expand our reach, particularly to low\-income workers who are disproportionately affected by diet\-related diseases.
At Foodsmart, our mission is to make nutritious food accessible and affordable for everyone, regardless of economic status. We are committed to a set of core values that shape our culture and work environment:
Customer First \- You start with the member and work backwards.
Make It Happen \- You act with urgency, use data, and hold high standards.
One Team \- You collaborate with respect and commit as a group.
Whether you're a dietitian, a commercial leader, or a technologist, working at Foodsmart means being part of a team that is passionate, supportive, and driven by a shared purpose. Join us in transforming the way people access and enjoy healthy food.
### About the Role:
Every QBR, renewal conversation, and RFP Foodsmart brings to a client or prospect is only as strong as the data behind it. We’re looking for a Director, Client Data Science to own that foundation: the reporting, automation, and metrics that make sure our external story holds up, without turning into a manual scramble every time a client asks a hard question.
You won’t own Foodsmart’s external narrative itself, that lives with our go\-to\-market and sales teams, but you’ll make sure it’s built on solid data, and you’ll act as the analytical consultant who helps sharpen how it’s told.
This is a high\-impact, domain\-owning role reporting to the VP of Analytics \& Data Science. You’ll be the connective point across the rest of the analytics team, pulling in expertise on member engagement and clinical outcomes as needed to round out the external story, while owning the client\-facing data foundation yourself. You’ll also build and own our system of client SLAs: what we can validate and stand behind today, and the standing recommendation on whether to take on new ones.
This is a lead role where leadership shows up in being the confident, credible voice in front of clients and partners rather than in managing a large team. It’s heavily hands\-on: you’ll build the reporting infrastructure yourself, work closely with Data Engineering to automate recurring weekly and monthly client reporting, and lean on AI as a genuine force multiplier, the main way this area scales, rather than just by adding headcount.
This is a hands\-on infrastructure and reporting role. We need someone who can move fluidly from dbt modeling to BI build\-out to actually sitting in front of a client, and who treats that full range as one job, not three.
We’re specifically looking for someone who has held an equivalent role at another health tech company: someone who has already lived the specific mix of clinical, operational, and commercial reporting that clients and payers expect, not someone learning the domain from scratch. Beyond that, we’re a small, flat, fast\-moving team that leans hard into AI\-native tooling. Hex, Omni, and Claude Code are our defaults for building and automating, and we want someone who thinks the same way.
### You will:
- Own Client Quarterly Business Review (QBR) automation end to end. Build the systems, using AI alongside other automation tooling, that turn QBR prep, including the slides and commentary, from a manual production process into something that runs largely on its own.
- Automate Foodsmart’s recurring weekly and monthly client reporting in partnership with Data Engineering, so routine reporting runs on its own instead of eating analyst time every cycle.
- Own the RFP data function: providing the analysis, benchmarks, and data cuts that Sales and the proposal team need, without owning the writing or the proposal process itself.
- Create and own a standard system of client SLAs: which commitments we can validate and stand behind today, and act as the point person for evaluating whether to adopt new SLA asks from prospects or clients.
- Build and maintain the foundational set of metrics Foodsmart uses externally: consistent, defensible numbers that Sales, Customer Success, RFP responses, and QBRs can all draw from rather than each function calculating its own version.
- Be conversant in Foodsmart’s clinical ROI story well enough to represent it confidently in QBRs and client conversations, partnering with our Senior Director of Clinical Intelligence on the underlying technical model and any deep\-dive analysis.
- Act as the data consultant behind Foodsmart’s external narrative: make sure it’s built on accurate data, and help sharpen how it’s told, without owning the story or the external materials yourself.
- Pull in expertise from across the analytics team, including member engagement and clinical outcomes, as needed to assemble a complete and accurate external story, rather than trying to own every domain’s data yourself.
- Partner directly with Customer Success and Sales leadership as their embedded analytical lead, attending cadences and understanding what clients and prospects are actually asking for.
- Represent Foodsmart directly in front of clients and prospects when needed, whether that’s presenting a QBR, supporting a sales call with data, or fielding technical questions during a deal.
### You are:
- An operator who thrives in flat, fast\-moving teams, comfortable owning a broad mandate with minimal oversight.
- A confident external communicator. You’re comfortable being in the room with clients, prospects, and partners, translating data into a clear, credible story on the spot, not just producing the underlying analysis.
- A systems thinker who’d rather build the infrastructure that makes dozens of QBRs easy than manually produce each one well. You see repetition as a signal to automate, not grind through.
- A trustworthy data steward. When you say a metric or an SLA is solid enough to put in front of a client or a payer, that means something, and you’re comfortable being the person who has to actually validate that before it goes external.
- A full\-stack analytics practitioner, strong across analytics engineering (dbt, semantic layer), BI and reporting, and enough data science fluency to know when a number needs real rigor behind it before it goes external.
- Deeply fluent with AI\-native tooling. You default to using Claude, Claude Code, and in\-BI AI agents to build and automate, and you have a point of view on how to use them to replace manual reporting work specifically.
- Someone who treats AI as a real lever on your own output. You default to using it to build, automate, and extend your reach, and you’d rather scale this area through leverage than through headcount.
### You have:
- Bachelor’s degree, ideally in a quantitative or technical field (e.g., Economics, Statistics, Computer Science, Operations Research, Applied Mathematics); Master’s degree is a plus.
- 12\+ years of experience in data science, analytics, or BI, with real experience owning client\-facing or external\-facing reporting. 4\+ years leading teams
- Experience in an equivalent role at another healthtech company, ideally one with a similar mix of clinical, operational, and commercial reporting demands.
- Direct experience supporting RFP processes with data and analysis, ideally in a healthcare, Medicaid, or payer\-adjacent context.
- Experience building or owning SLA frameworks, or a clear point of view on how to structure one from scratch.
- Comfort discussing clinical ROI and outcomes methodology at a business level. You don’t need to own the technical model, that sits with our Senior Director of Clinical Intelligence, but you need to represent it credibly to clients and prospects.
- Experience partnering directly with Sales and Customer Success leadership as their analytical lead.
- Expert\-level proficiency in SQL and working knowledge in Python.
- Deep, production\-level experience with dbt, including source and metric modeling, testing, documentation, and semantic layer design.
- Experience with context engineering for BI and AI self\-service: writing semantic layer definitions, metric descriptions, and data model documentation that enables reliable AI\-assisted querying (Omni, Looker, or equivalent).
- Proven fluency with AI\-native developer and analyst tooling (Claude, Claude Code, Cursor, Hex AI Agent, Omni AI, or equivalent) used in production analytical workflows.
- Experience automating recurring reporting deliverables (QBRs, board decks, client\-facing dashboards) using AI or workflow tooling, not just building the underlying dashboard.
- Excellent written and verbal communication skills, with genuine comfort presenting data\-driven narratives to external, non\-technical audiences.
Role: Director, Client Data Science
Location: Remote, USA
Base Salary Range: $190,000 \- $220,000 \+ bonus \+ benefits
Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries at our headquarters in San Francisco, California. Individual pay is determined by work location, job\-related skills, experience, and relevant education.
### About our benefits and perks:
✅ Remote\-First Company
✅ Unlimited PTO
✅ Flexible \& remote location
✅ Healthcare Coverage (Medical, Dental, Vision)
✅ 401k \& bonus
✅ Registered Dietitian Sessions
Foodsmart is an Equal Opportunity Employer. It is our firm policy to extend equal employment and advancement opportunity to all applicants and employees without regard to race, color, national origin, citizenship status, religious creed, age, sex (including pregnancy, childbirth, breastfeeding, medical conditions related to pregnancy, childbirth and/or breastfeeding), gender, gender identity and expression, sexual orientation, marital status, disability (physical or mental) and/or a medical condition, genetic information, ancestry, veteran status or service in the uniformed services, and any other characteristic protected by applicable federal, state or local law.
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 $190K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Foodsmart, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($205K) sits 5% below the category median. Disclosed range: $190K to $220K.
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.
Foodsmart AI Hiring
Foodsmart has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $220K - $220K.
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 AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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