{"id":933545,"url":"https://alion.io/job/mathco-data-scientist","title":"Data Scientist","company":{"id":455909,"name":"MathCo","domain":"mathco.com","url":"https://alion.io/company/mathco","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":17500,"max_usd":48000,"period":"year","method":"role_country_seniority_unknown","sample_n":171},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Explainable 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patient journey analytics to identify treatment initiation, progression, switching, discontinuation and drop-off points across patient cohorts.\nPerformed treatment adherence, persistence and days-on-therapy analysis to identify factors influencing patient continuation and treatment outcomes.\nApplied survival analysis techniques to evaluate time-to-event outcomes, treatment persistence and patient-level healthcare journeys.\nDeveloped sequence-based analytical approaches to understand longitudinal patient treatment patterns and transitions across therapies.\nApplied hypothesis testing, probability, statistics and machine learning to formulate business problems into analytical hypotheses and proofs-of-concept.\nDeveloped predictive and statistical models using Python and SQL, including feature engineering, model development, validation and interpretation.\nWorked with large-scale healthcare datasets involving data extraction, integration, cleansing, transformation and cohort construction for RWE analytics.\nTranslated complex analytical findings into business recommendations and actionable insights for technical and non-technical stakeholders.\nCreated executive-ready visualisations and analytical dashboards using Tableau, Power BI, Matplotlib and Seaborn.\nCollaborated with cross-functional stakeholders to understand business requirements, define analytical approaches, prioritise deliverables and manage project risks.\nFollowed modern software engineering practices including Git/version control, peer code reviews, reusable code, documentation, refactoring and collaborative development.\nPrepared technical documentation covering business understanding, data preparation, methodology, experimentation, model validation and recommendations, following CRISP-DM-aligned practices.\nProvided technical mentorship and peer reviews to junior data scientists, improving analytical quality and development practices across the team.\nLed multiple analytical workstreams by breaking complex business problems into smaller components, allocating tasks, tracking progress and ensuring timely delivery.\nResearched emerging Machine Learning, Deep Learning, NLP, explainable AI and advanced analytics techniques and evaluated their applicability to healthcare and pharmaceutical use cases.\nCommunicated analytical insights through storyboarding and structured presentations, enabling non-technical stakeholders to understand model outputs, business implications and recommended actions.","description_format":"text","description_chars":2864,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Consulting & Integration","Analytics & BI 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