{"id":24642,"url":"https://alion.io/job/shepherd-actuarial-data-science-lead","title":"Actuarial Data Science Lead","company":{"id":6727,"name":"Shepherd","domain":"shepherdinsurance.com","url":"https://alion.io/company/shepherd","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"C","score":61,"open_postings":20,"ghost_share":0.65,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-29T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States","San Francisco, United States","Chicago, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":240000,"currency":"USD","period":"year","gross":null,"usd_annual":240000},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"AWS","optional":true},{"name":"NLP","optional":true}],"status":"live","first_seen_at":"2026-07-01T19:10:18Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-09-29T19:24:15Z","board_verified":true,"closed_at":null,"days_open":90,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":89},"description":"What We Do\nYesterday's insurance wasn't built for today's risk. We see it in the data and we feel it in the field. Emerging technology can reinvent how risk is priced and managed, faster and smarter, anchored in proven expertise. First-movers will define the next era of commercial risk management, and Shepherd is building it.\nShepherd is a technology-driven Managing General Underwriter (MGU) transforming commercial Property & Casualty insurance for high-hazard industries. Our mission is to make risk frictionless for the builders and operators shaping the physical world, protecting progress from concept through construction and into decades of operation.\nWe're building the fastest, smartest commercial risk platform, where underwriting expertise, data, and automation work together to deliver:\nFaster decisions\n\nSmarter, more accurate pricing\n\nBetter risk outcomes\n\nWith Shepherd, safety, speed, and quality no longer trade off against one another. They compound. We're not just modernizing insurance products. We're building the risk infrastructure for the next generation of financial services, where technology, underwriting, and partnerships operate in harmony to support the world's most important industries and the progress they make possible.\nOur Investors\nIn March 2026, Shepherd raised a $42M Series B - bringing total funding to over $60M - led by Intact Private Capital, the investment arm of one of the largest insurers in the world. Intact is not only our lead investor but also a carrier partner, a testament to the confidence the incumbent industry has in what we're building. Our investors:\nIntact Private Capital, led our Series B round\n\nCostanoa Ventures, led our Series A round\n\nSpark Capital, led our Seed round\n\nSusa Ventures, lead our Pre-Seed round\n\nY Combinator\n\nAnd several others\n\nOur Team\nWe're a team of technologists and insurance enthusiasts, bridging the two worlds together. Check out our About page to learn more.\nAbout the Role\nShepherd is building the data infrastructure and predictive models that power modern commercial insurance. As an Actuarial Data Science Lead on the Actuarial & Predictive Analytics team, you will own the development of pricing models starting with commercial auto, one of our highest-volume and most data-rich lines. You'll directly shape the quality of the book we write and the products we bring to market.\nThis is a high-impact, individual-contributor role for someone who thrives at the intersection of statistical rigor and shipping real products. You will work closely with actuaries, underwriters, and engineers to turn data into decisions.\nWhat You'll Do\nOwn commercial auto pricing models end-to-end from feature development through deployment and iterate on them as the book grows and new data sources come online\n\nBuild and deploy predictive models build and deploy loss cost models that set pricing for Shepherd's commercial auto book\n\nDesign and maintain feature pipelines that transform raw submission, claims, and third-party data into model-ready inputs\n\nCollaborate with actuaries and underwriters to translate domain expertise into model features and validate outputs against real-world outcomes\n\nDevelop model monitoring frameworks to track drift, performance degradation, and calibration over time\n\nRun experiments and back-tests to quantify model impact on loss ratios, pricing accuracy, and portfolio quality\n\nCommunicate findings clearly to technical and non-technical stakeholders through concise documentation and presentations\n\nWhat We're Looking For\nMust-Haves\n7+ years of professional experience building and deploying personal auto or commercial lines predictive pricing models in production\n\nFamiliarity with actuarial concepts (loss development, exposure rating, credibility)\n\nStrong foundation in statistics: GLMs, GBDTs, time series analysis, heavy tail distributions, and Bayesian methods\n\nProficiency in Python and SQL\n\nACAS/FCAS actuarial designation\n\nExperience with feature engineering on messy, real-world, small data\n\nAbility to reason from first principles and communicate results crisply to non-technical audiences\n\nAI-native mindset: you already use LLMs and AI tools to accelerate your own work\n\nExperience managing a small team or project\n\nNice-to-Haves\nExperience in insurance, insurtech, fintech, or other regulated industries\n\nExposure to telematics pricing models\n\nExperience with NLP/document extraction from unstructured insurance submissions\n\nPrior work with model deployment infrastructure (AWS)\n\nHow we work\nShepherd runs on four values. Here's what each one means in this seat.\nThink big, build big. We exist to protect progress and the industries that rely on it. The work here is aimed at a system that runs on its own, and the roadmap gets sequenced backward from that rather than forward from what's easy.\n\nWin together. We rise as one. We support each other, raise the bar, and celebrate collective success. As the first PM you set a standard the rest of the team inherits, and the milestones belong to the team rather than to product.\n\nCross the aisle. Collaboration wins. We listen deeply, work across boundaries, and prioritize shared success over individual lanes. The best product calls here come from engineers who've sat with underwriters and underwriters who understand where the model breaks, and much of this job is listening closely enough on both sides to make that happen.\n\nGo get it. We act with urgency, move with confidence, take smart risks, and push forward with intention. Nobody hands you the roadmap, the data, or the meeting invite. You pull the failing runs, book the time with the underwriters, and decide what matters.\n\nBenefits\nPremium Healthcare\n100% contribution to top-tier health, dental, and vision\nFertility benefits and family building support\nUnlimited PTO\nFlexibility to take the time off, recharge, and perform\nDaily lunches, dinners, and snacks\nWe work together, and enjoy meals together too\nSF, NYC, Dallas-Fort Worth, Chicago and LA Offices\nProfessional Development\nAccess to premium coaching, including leadership development\nCompetitive 401(k) Plan\nDog-friendly office\nPlenty of dogs to play with and make friends with in the SF office","description_format":"text","description_chars":6207,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Professional development","Unlimited PTO"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Property & Casualty Insurance","InsurTech"],"lifecycle":[{"event":"open","at":"2026-07-01T19:10:18Z"}],"liveness":{"score":8,"band":"cold","label":"Long shot","p_open":1,"p_active":0.293,"p_room":0.28,"age_days":89,"expected_fill_days":32,"reasons":["conf:2","stale_co","win:tail","crowd:"],"computed_at":"2026-09-29T05:45:00Z"},"pay":{"stated_usd_annual":240000,"is_top_pay":true},"html_url":"https://alion.io/job/shepherd-actuarial-data-science-lead","json_url":"https://alion.io/job/shepherd-actuarial-data-science-lead.json","meta":{"generated_at":"2026-09-30T01:51:21Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1480,"day_limit":5000,"remaining_today":3520,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}