{"id":2063418,"url":"https://alion.io/job/keep-company-founding-data-machine-learning-lead","title":"Founding Data & Machine Learning Lead","company":{"id":3865847,"name":"Keep Company","domain":"keep-company.com","url":"https://alion.io/company/keep-company","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":140000,"max":null,"currency":"USD","period":"year","gross":null,"usd_annual":140000},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Amazon Aurora","optional":false},{"name":"Amazon S3","optional":false},{"name":"dbt","optional":false},{"name":"DuckDB","optional":false},{"name":"PostgreSQL","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-04T15:33:19Z","employer_posted_date":"2026-10-04","last_verified_at":"2026-10-12T09:57:29Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"Brass Tacks\nRemote - ideally East Coast. Able and willing to travel to our Bethesda HQ monthly.\n$140K base \nMeaningful equity in our early-stage, venture-backed company\nWe pay 100% of the premium for two UHC Gold plans and 50% for your dependents. Same approach for dental and vision\n401(k) + FSA + HSA + Commuter benefits \nHigh accountability + high flexibility culture \nAbout Keep Company\nKeep Company is on a mission to make human connection an intentional, scalable asset in every workplace. As a venture-backed SaaS platform recognized by Techstars, Forbes, and SHRM, we blend technology with coaching and mentoring to deliver transformative, measurable employee experiences. We help organizations accelerate culture, retention, and productivity in the moments that matter most.\nKeep Company is the platform for people programs, helping professional services firms build and scale the relationships that develop their people and keep top talent.\nAs AI transforms how work gets done, the relationships and human skills that build judgment, confidence, and performance are increasingly important to how firms develop their people and differentiate themselves. Keep Company helps firms create those relationships by design - across career stages and seasons - so they can develop their people, keep the talent who set them apart, and stay ahead as work changes.\nOur configurable technology, embedded expertise, and actionable insights make it easier to build, manage, measure, and scale relationship-based programs across the firm. Organizations can use Keep Company’s platform to power their own programs - from mentoring and group coaching to peer groups and ERGs - or launch full-service programs designed and live-facilitated by our team. We combine technology that makes development easier to scale with human expertise where it matters most.\nThe Role\nThis is Keep Company’s first dedicated data hire. It is a hands-on build role: initially weighted toward data engineering and analytics infrastructure, with meaningful ownership of model development, evaluation, and strategy as the foundation matures.\nYou will own how data moves through Keep Company: from source systems and product events to clean, trusted, member-centric datasets, reporting, and early predictive use cases.\nYou will partner closely with Product and Engineering to define the signals we should capture, build the infrastructure that makes those signals usable, and evaluate where models can improve matching, recommendations, and intervention decisions.\nThe first phase of the role will be primarily focused on the foundations: pipelines, data modeling, instrumentation, data quality, and reusable reporting. Over time, you will lead the strategy and development of models that help Keep Company identify risks, improve relationship outcomes, and make more useful recommendations.\nWhat You'll Own\nData engineering and foundations\nOwn Keep Company’s analytical data foundation, including the warehouse, transformations, pipelines, data quality, and documentation.\nEstablish MotherDuck as a reliable analytical source of truth, including dbt models, source definitions, and durable datasets for internal and in-product use.\nPartner with Engineering to improve how product, program, survey, HRIS, import, and coach data is collected, structured, and maintained.\nBuild and maintain pipelines across current inputs, including WorkOS/HRIS integrations, CSV imports, surveys, forms, and platform activity.\nBring historical data into a clean, member-centric structure that connects attendance, registrations, survey responses, coaching inputs, relationship activity, and outcomes.\nDefine and maintain a shared taxonomy for constructs, questions, scales, programs, cohorts, and other historical data that is currently inconsistent.\nPartner with Product and Engineering to define product instrumentation: what actions, milestones, decisions, and outcomes should be captured, where they should live, and how they can be used.\nReporting, measurement, and insight\nBuild reusable measurement layers and reporting products rather than one-off analyses.\nDevelop reporting that helps clients understand relationship participation, engagement, matching quality, and program outcomes.\nEstablish an internal operating cadence for data: trusted dashboards and recurring analysis that leadership uses to make decisions.\nPartner with Product, Programs, Client Success, and leadership to define meaningful metrics and distinguish signals from noise.\nBuild analysis that tests and improves Keep Company’s core assumptions, including how relationship design and matching affect engagement, retention, and development.\nModel development and data strategy\nOwn the strategy for moving from descriptive reporting toward predictive and recommendation capabilities.\nDocument, evaluate, and improve the matching logic currently used in the product, including its assumptions, objective functions, scoring logic, failure modes, and quality tradeoffs.\nBuild and test early models, heuristics, and prototypes that improve matching, identify risk, or support more useful recommendations.\nDefine what additional data is required to support future prediction and recommendation use cases-and prioritize the collection and instrumentation needed to obtain it.\nDevelop an experimentation approach for model-backed features: establish baselines, define success criteria, monitor quality, and improve systems over time.\nIdentify where a heuristic is sufficient and where a more rigorous model is warranted.\nBuild toward future use cases such as identifying low-quality matches before launch, detecting disengagement or at-risk programs early, and recommending the appropriate relationship type for a given employee lifecycle moment.\nEnsure predictive capabilities can be introduced thoughtfully, including privacy protections, tenant isolation, explainability, confidence thresholds, and paths for clients with AI restrictions.\nWho You Are\nMust Have\n4-6 years of strong hands-on experience in data engineering, product analytics, decision science and/or data science, at a technology company with a track record of influencing product decisions.\nExperience with transformation tooling such as dbt, and with building reliable ingestion and transformation pipelines.\nDeep SQL fluency and experience designing and maintaining warehouse data models.\nExperience working with messy, incomplete, inconsistent, or historically unstandardized data.\nStrong background designing well-powered A/B tests, diagnosing bias and variance issues, and interpreting results under real-world conditions. \nAbility to translate business and product questions into durable datasets, metrics, reporting, and decision-making tools.\nExperience partnering with Product and Engineering to define instrumentation and turn product behavior into usable data.\nWorking knowledge of statistics, experimentation, clustering, matching, and predictive modeling-enough to build or evaluate early models and set a sound modeling roadmap.\nGood judgment about data quality, privacy, sensitive attributes, tenant isolation, and consent-based product design.\nStrong communication skills and comfort explaining data, tradeoffs, uncertainty, and model behavior to non-technical stakeholders.\nPreferred\nExperience with MotherDuck, DuckDB, Aurora/Postgres-derived data, S3, Parquet, WorkOS, Hex, or similar tools.\nExperience building recommendation, matching, prioritization, or prediction systems.\nExperience evaluating or improving a heuristic-based system before moving to more formal machine-learning approaches.\nExperience working alongside ML engineers on production models, including offline evaluation, online metrics, and guardrails. \nExperience building data products or analytics capabilities in a B2B SaaS environment.\nExperience with employee, engagement, professional-services, people, or relationship data.\nExperience using modern AI tools or open-weight models responsibly in product or data workflows.\nExperience helping build or lead a growing data, analytics, or BI function.\nWhat Success Looks Like\nIn the first 6-12 months, you will have:\nCreated a trusted, documented analytical foundation for Keep Company’s core data.\nImproved the reliability and usability of historical member, program, survey, and coaching data.\nDefined the most important product and outcome signals Keep Company needs to capture.\nDelivered reusable internal and client-facing reporting built on durable datasets.\nEvaluated the current matching approach and established a clear roadmap for model development.\nTested at least one early predictive or recommendation-oriented use case with appropriate measurement and safeguards.\nMade data a consistent input into product, client, and leadership decisions.","description_format":"text","description_chars":8785,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-08T02:50:35Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":7,"expected_fill_days":27,"reasons":["conf:3","win:early"],"computed_at":"2026-10-12T06:55:09Z"},"pay":{"stated_usd_annual":140000,"is_top_pay":false},"html_url":"https://alion.io/job/keep-company-founding-data-machine-learning-lead","json_url":"https://alion.io/job/keep-company-founding-data-machine-learning-lead.json","meta":{"generated_at":"2026-10-12T10:49:57Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":16762,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-13T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":3865847},"rest":"https://alion.io/mcp/rest/get_company?id=3865847"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fkeep-company-founding-data-machine-learning-lead"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fkeep-company-founding-data-machine-learning-lead"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fkeep-company-founding-data-machine-learning-lead"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/keep-company-founding-data-machine-learning-lead\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fkeep-company-founding-data-machine-learning-lead"}]}