{"id":4117386,"url":"https://alion.io/job/bain-company-lead-full-stack-data-scientist","title":"Lead, Full-Stack Data Scientist","company":{"id":1755715,"name":"Bain & Company","domain":"bain.com","url":"https://alion.io/company/bain-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":null},"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":["Boston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":173000,"max":173000,"currency":"USD","period":"year","gross":null,"usd_annual":173000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Docker","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GitHub","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-11-11T23:27:57Z","employer_posted_date":null,"last_verified_at":"2026-11-11T23:27:57Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Bain & Company is hiring a Lead, Full-Stack Data Scientist for its Data Science & Machine Learning Engineering team. The role designs, develops, and deploys data science and AI solutions for client and internal needs, collaborating with consulting and engineering teams to address business challenges.\n\nResponsibilities\nDevelop data, technology and AI solutions, leveraging a spectrum of techniques including data science, machine learning, AI algorithms to address large-scale enterprise challenges and enable production systems\nDevelop and maintain long-lasting models, algorithms, and tools that support internal or client needs\nCollaborate closely with and influence general consulting teams to identify analytics solutions for client business problems and to execute those solutions\nLeverage AI tools with guardrails to accelerate and amplify design, development and implementation phases across the life cycle\nEvaluate where generative AI offers a better path to a client solution, and apply techniques such as prompt engineering, retrieval-augmented generation, and model fine-tuning where appropriate\nCollaborate with technology leaders to develop and advocate for cutting-edge data science concepts to both technical audiences and business stakeholders\nCollaborate with data engineers to ensure data pipelines and platforms support modeling, experimentation, and analytics use cases\nSupport data transformations at scale, including cleaning, enriching, de-duping, joining, and correlating structured, semi-structured, or unstructured data for modeling and analysis\nDefine and implement new and innovative modeling techniques, experimentation strategies, and analytics workflows within Bain's full model development life cycle, including documentation, unit/integration testing, and reviews\nSupport model deployment and operationalization in collaboration with engineering teams, ensuring scalable data science solutions\nCommunicate clearly with technical and non-technical stakeholders; lead working sessions, present recommendations, and write crisp technical documentation\nCollaborate with client data engineering and analytics teams and, when needed, contribute to data acquisition, extraction, and preparation to ensure data is fit for modeling and analysis\nEstablish and promote best practices for model development, documentation, and reuse, enabling scalable and repeatable approaches across industries and use cases\nHelp prioritize and scope data science initiatives by translating complex client business problems into actionable projects with clear execution paths\nProvide technical guidance to external clients and internal stakeholders at Bain\nContribute to industry-leading innovations that translate into meaningful impact for clients in casework\nStay current with emerging trends and technologies in data science, and proactively identify opportunities to enhance the capabilities of the analytics platform\n\nSkills\nA strong generalist background with proficiency across core data science methods, including forecasting, statistical modeling, optimization modeling and machine learning\nAn interest in applying your expertise across a wide array of industries and problems\nStrong experience working in common data science query and programming languages (Python required, at least working knowledge of SQL)\nHands-on experience with at least two of the following problem types, with breadth of experience across associated tools and techniques\nExperience building and evaluating LLM-based applications, including prompt optimization\nExperience in AI-native delivery, such as using AI-assisted coding tools to accelerate modeling work, generate and test hypotheses, and prototype analytical approaches\nHands-on experience collaborating with engineering teams to take models into production\nExperience working in agile, cross-functional team environments\nStrong interpersonal and communication skills, including the ability to discuss technicalities of solutions, algorithms, and techniques with colleagues and clients\nCuriosity, proactivity, and critical thinking\nAbility to collaborate with people at all levels and with multi-office/region teams\nStrong stakeholder management skills, with both internal leadership and with clients\nProven ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a collaborative team\nAdvanced degree in Data Science, Computer Science, Mathematics, Engineering, Econometrics, Statistics or Information Sciences\n3+ years of experience in advanced analytics or related field\nWorking knowledge of Bayesian methods\nExperience deploying ML models using containerization (Docker, Kubernetes, Terraform) and MLOps tooling such as MLflow and GitHub Actions\nExperience managing data workflows and pipelines for modeling\nWorking knowledge of SQL or NoSQL databases (e.g., PostgreSQL, SQL Server, Oracle, MySQL, Redis, MongoDB, Elasticsearch, Snowflake, or similar)\nExperience scaling model training and inference on large datasets, and optimizing pipelines for performance in production environments\nExperience with building complete web apps using coding assistants\nExperience building agentic harnesses and orchestration of agentic processes\nPhD is a plus\n\nBenefits\nAnnual discretionary performance bonus\n4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date\nBain & Company's comprehensive benefits and wellness program\nBain pays 100% of individual employee premiums for medical, dental and vision programs\nGenerous paid time off, including parental leave, sick leave and paid holidays\nPaid Life and Long-Term Disability insurance\nAnnual fitness reimbursements\n\nCompany Overview\nBain & Company is a global management consulting firm, a top advisor to the world's leading private, public and non-profit organizations. It was founded in 1973, and is headquartered in Boston, Massachusetts, USA, with a workforce of 10001+ employees. Its website is https://www.bain.com.\n\nCompany H1B Sponsorship\nBain & Company has a track record of offering H1B sponsorships, with 110 in 2026, 78 in 2025, 86 in 2024, 138 in 2023, 89 in 2022, 53 in 2021, 60 in 2020. 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