{"id":3388054,"url":"https://alion.io/job/weekday-data-science-manager","title":"Data Science Manager","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":81,"open_postings":91,"ghost_share":0,"stale_share":0.978,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Pune, 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":"Machine Learning","optional":false},{"name":"Microsoft Excel","optional":false},{"name":"Microsoft Office","optional":false},{"name":"Python","optional":false},{"name":"Scrum","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-28T08:40:56Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-30T16:56:58Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"This role is for one of Weekday’s clients\nMin Experience: 8+ years\nLocation: Pune, Maharashtra, India\nJobType: full-time\nWe are looking for an experienced and results-oriented Data Science Manager to lead the delivery of data science and machine learning projects, with a focus on pharmaceutical and healthcare-related business challenges. The role involves collaborating with engagement managers, account delivery teams, and client stakeholders to understand business requirements, design analytical solutions, and ensure high-quality project execution.\nThe ideal candidate will combine strong expertise in probability, statistics, and practical machine learning with working knowledge of SQL, Python, and project management methodologies. They should be capable of translating business problems into effective data science solutions, guiding technical teams, managing client expectations, and ensuring projects are delivered within the agreed scope, timelines, and quality standards.\nRequirements\nKey Responsibilities\n1. Client Engagement and Requirement Gathering\nCollaborate with engagement managers, account delivery managers, and client stakeholders to understand business objectives, project requirements, and expected outcomes.\nTranslate business requirements into well-defined analytical problems and data science project deliverables.\nMaintain effective communication with clients throughout the project lifecycle and ensure alignment on scope, milestones, and priorities.\nLead client meetings, weekly business reviews, and project discussions to communicate progress, address challenges, and agree on next steps.\n2. Data Science Solution Design\nDevelop appropriate data science and machine learning approaches to address client business challenges.\nApply advanced knowledge of probability and statistics to analyse data, validate assumptions, and generate meaningful insights.\nDesign practical machine learning solutions while accounting for common pitfalls, model limitations, data quality issues, and potential sources of bias.\nGuide the selection of suitable analytical methodologies, modelling techniques, and evaluation metrics based on project objectives.\nUse SQL and Python to support data exploration, analysis, model development, and validation as required.\n3. Project Planning and Delivery Management\nDevelop comprehensive project plans covering scope, objectives, resource allocation, milestones, timelines, and deliverables.\nAllocate tasks to team members based on their skills, experience, and project requirements.\nLead project execution and monitor progress against agreed plans, identifying risks and implementing corrective actions where necessary.\nTrack project milestones, manage dependencies, and ensure timely delivery without compromising quality.\nApply appropriate project management methodologies and tools to improve delivery efficiency and accountability.\n4. Quality Assurance and Performance Monitoring\nTake ownership of project delivery quality and ensure all deliverables meet established standards and client expectations.\nMonitor delivery performance across key metrics and scorecard dimensions, report progress accurately, and develop action plans to address gaps.\nEstablish and enforce quality assurance practices, including peer reviews, documentation standards, coding best practices, and quality checklists.\nReview analytical outputs, code, presentations, reports, spreadsheets, and client communications before delivery.\nIdentify opportunities to improve project processes, analytical quality, and overall delivery performance.\n5. Team Leadership and Development\nProvide technical and business guidance to data scientists, analysts, and other delivery team members.\nLead internal scrum meetings, daily stand-ups, and project planning discussions to maintain alignment and execution discipline.\nCoach team members, support their professional development, and encourage continuous learning.\nFoster a high-performance work environment built on collaboration, accountability, mutual trust, and respect.\nEncourage experimentation, innovation, and the adoption of improved data science and project delivery practices.\n6. Pharmaceutical Domain Application\nApply data science methodologies to business problems within the pharmaceutical and healthcare ecosystem.\nUnderstand client objectives and translate domain-specific requirements into relevant analytical approaches.\nSupport the development of data-driven recommendations that improve business decision-making and project outcomes.\nMust-Have Skills and Qualifications\nPharmaceutical Domain: Relevant exposure to the pharmaceutical industry or pharma-related data science projects.\nProbability and Statistics: Advanced knowledge of statistical methods, probability theory, and analytical techniques.\nMachine Learning: Advanced understanding of practical machine learning methods, model evaluation, common pitfalls, and mitigation approaches.\nSQL and Python: Intermediate proficiency in SQL and Python for data analysis, querying, and analytical problem-solving.\nProject Management: Intermediate knowledge of project management methodologies, planning tools, milestone tracking, and delivery processes.\nClient and Stakeholder Management: Ability to gather requirements, communicate progress, manage expectations, and maintain strong client relationships.\nLeadership: Experience guiding team members, reviewing deliverables, and maintaining high standards of execution.\nMS Office: Intermediate proficiency in Microsoft Excel, PowerPoint, and Word for analysis, reporting, and presentations.\n Quality and Delivery Management: Ability to monitor project performance, identify delivery risks, and implement quality improvement initiatives.","description_format":"text","description_chars":5728,"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":["Continuous learning","Professional 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