{"id":2428019,"url":"https://alion.io/job/trinet-lead-data-scientist","title":"Lead Data Scientist","company":{"id":1462,"name":"TriNet","domain":"trinet.com","url":"https://alion.io/company/trinet","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":16},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"GCP","optional":false},{"name":"Interpretability","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"Unstructured.io","optional":false}],"status":"live","first_seen_at":"2026-06-10T09:25:16Z","employer_posted_date":"2026-06-10","last_verified_at":"2026-10-13T15:51:26Z","board_verified":true,"closed_at":null,"days_open":125,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":125},"description":"TriNet is a leading provider of comprehensive human resources solutions for small to midsize businesses (SMBs). We enhance business productivity by enabling our clients to outsource their HR function to one strategic partner and allowing them to focus on operating and growing their core businesses. Our full-service HR solutions include features such as payroll processing, human capital consulting, employment law compliance and employee benefits, including health insurance, retirement plans and workers’ compensation insurance.\nTriNet has a nationwide presence and an experienced executive team. Our stock is publicly traded on the NYSE under the ticker symbol TNET. If you’re passionate about innovation and making an impact on the large SMB market, come join us as we power our clients’ business success with extraordinary HR.\nDon't meet every single requirement? Studies have shown that many potential applicants discourage themselves from applying to jobs unless they meet every single requirement. TriNet always strives to hire the most qualified candidate for a particular role, ensuring we deliver outstanding results for our small and medium-size customers. So if you're excited about this role but your past experience doesn't align perfectly with every single qualification in the job description, nobody’s perfect - and we encourage you to apply. You may just be the right candidate for this or other roles.\nA Brief Overview\nAs a member of the Enterprise Data & Analytics team, the Lead Data Scientist is responsible for applying advanced analytical, statistical, and machine learning techniques to solve complex business problems. This role partners with cross-functional stakeholders to frame business questions, develop predictive and prescriptive models, and deliver insights that drive data-informed decision-making.\nThis individual will lead analytical solution design end-to-end, from problem framing and data exploration through modelling, validation, and deployment while ensuring results are interpretable, scalable, and aligned to business outcomes.\nWhat You Will Do\nAdvanced Analytics & Modelling\nDevelop, test, and deploy statistical models, machine learning models, and analytical frameworks.\nApply techniques such as regression, classification, clustering, forecasting, and optimisation.\nEnsure models are explainable, reliable, and aligned with business objectives.\nBusiness Problem Framing\nPartner with stakeholders to define analytical problems, hypotheses, and success criteria.\nTranslate ambiguous business questions into well-structured analytical approaches.\nIdentify key drivers, risks, and opportunities through data exploration and hypothesis testing.\nGenerative AI & Unstructured Data\nAbility to work with unstructured data and apply NLP and LLM-based techniques to solve complex business problems.\nHands-on experience with Generative AI approaches, including prompt engineering, retrieval-augmented generation (RAG), and evaluation of LLM outputs for accuracy, bias and business relevance.\nStrong understanding of how GenAI and LLM solutions are designed and integrated, and ability to partner with engineering teams to deliver scalable, governed AI solutions.\nData Exploration & Feature Engineering\nPerform exploratory data analysis to uncover patterns, trends, and anomalies.\nEngineer features from structured and unstructured data sources.\nAssess data quality, bias, and limitations in analytical outputs.\nModel Validation & Operationalisation\nValidate model performance using appropriate metrics and testing approaches.\nCollaborate with data engineering and BI teams to operationalise models and insights.\nMonitor model performance over time and recalibrate as needed.\nCommunication & Storytelling\nCommunicate findings, insights, and recommendations to technical and non-technical audiences.\nTranslate complex analytical results into clear, actionable business narratives.\nSupport decision-making with scenario analysis and impact assessments.\nContinuous Improvement & Innovation\nStay current with evolving data science methods, tools, and industry trends.\nIdentify opportunities to apply advanced analytics and AI to new business problems.\nPromote analytical best practices across the organisation.\nEducation Qualifications\nBachelor’s Degree in Data Science, Statistics, Mathematics, Computer Science, or related field\nMaster’s Degree or PhD preferred\nExperience Qualifications\nTypically 8+ years of experience in data science, advanced analytics, or applied statistics\nHands-on experience with machine learning and statistical modelling techniques\nExperience working with large, complex datasets in an enterprise environment\nExperience partnering with business teams to drive measurable outcomes\nSkills and Abilities\nStrong proficiency in Python, R, or similar analytical programming languages\nStrong foundation in statistics and machine learning\nAbility to frame and solve ambiguous business problems\nExperience balancing model sophistication with interpretability\nStrong critical thinking and problem-solving skills\nAbility to communicate complex concepts clearly and effectively\nStrong collaboration and stakeholder engagement skills\nCommitment to high professional and ethical standards\nLicenses and Certifications (preferred)\nMicrosoft Certified: Azure Data Scientist Associate (DP-100) or equivalent \nAWS Certified Machine Learning-Specialty or equivalent cloud ML certification\nGoogle Cloud Professional Machine Learning Engineer \nCertified Analytics Professional (CAP/CAP-X) by INFORMS preferred \nTensorFlow or advanced machine learning certifications a plus\nTravel Requirements\nMinimal Travel Required\nWork Environment:\nWork in a clean, pleasant, and comfortable office work setting. The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable persons with disabilities to perform the essential functions.\nThis position is 100% in office.\nPlease Note: TriNet reserves the right to change or modify job duties and assignments at any time. The above job description is not all encompassing. Position functions and qualifications may vary depending on business necessity.\nTriNet is an Equal Opportunity Employer and does not discriminate against applicants based on race, religion, color, disability, medical condition, legally protected genetic information, national origin, gender, sexual orientation, marital status, gender identity or expression, sex (including pregnancy, childbirth or related medical conditions), age, veteran status or other legally protected characteristics. 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