Data Scientist

Data Scientist Department Data & Analytics Industry Healthcare Technology / Digital Health / HealthTech Experience 1 – 2 Years Employment Type Full-time Reporting To Analytics Lead / Head of Data & Analytics Location / Work Mode Mumbai (Andheri) / In Office About the Platform We are building a digital content platform for Healthcare Professionals (HCPs), designed to deliver relevant clinical knowledge, medical updates, continuing medical education (CME), professional communities, conferences, and patient education resources. The platform brings together multiple content formats and engagement channels to help healthcare professionals access relevant, high-quality information throughout their professional journey. We are looking for a Data Analyst who can transform user behaviour, content consumption, and campaign performance data into actionable business insights. The ideal candidate will work closely with Product, Engineering, Content, Marketing, and CRM teams to improve platform adoption, user engagement, content discoverability, and retention. Role Objective The primary objective is to establish a data-driven decision-making framework across the platform by: Measuring user acquisition, activation, engagement, retention, and reactivation. Understanding how HCPs interact with different platform features and content categories. Evaluating the effectiveness of CRM campaigns and communication channels. Identifying opportunities to improve MAU, session frequency, content consumption, and retention. Building automated dashboards and recurring reports for business stakeholders. Ensuring data accuracy, event tracking consistency, and metric standardization. Key Responsibilities Product Analytics & User Behaviour Analyze user journeys from app launch and registration to feature adoption, content consumption, and repeat visits. Track DAU, WAU, MAU, sessions per user, session duration, and retention rates. Identify frequently used, underutilized, and abandoned platform features. Analyze feature adoption across HCP segments, specialties, cohorts, and activity levels. Identify drop-off points and recommend improvements to product flows. Build cohort analyses for first-time, returning, dormant, and reactivated users. Distinguish organic from campaign-driven engagement and identify channels contributing to sustained usage. Conduct funnel analysis and evaluate feature launches and product changes. Content Performance & Consumption Analytics Measure content reach, unique viewers, total views, consumption frequency, completion rates, and repeat consumption. Identify content with high reach but low engagement, and content that drives repeat visits. Analyze performance by content type, specialty, topic, publication date, and HCP segment. Identify content inventory that is underutilized or receives limited engagement. Evaluate the relationship between publishing frequency and user engagement. Measure time to first engagement after publication. Build content performance scorecards and recommend improvements to distribution and discoverability. CRM & Campaign Analytics Analyze push notification, email, in-app messaging, and other available campaign channels. Track delivery, reach, opens/views, clicks, CTR, downstream content consumption, and attributable engagement. Compare campaign performance by audience segment, HCP specialty, activity cluster, content type, and time window. Evaluate campaign-driven app launches, sessions, content views, and repeat visits. Measure reactivation campaigns targeting dormant or inactive users. Identify effective campaign formats, content categories, timing, and audience segments. Support behavioural segmentation, including Recency, Frequency, and Monetary Value (RFM) where relevant. Evaluate campaign fatigue and declining engagement; support A/B tests and controlled experiments. Develop attribution methods to distinguish campaign-driven activity from organic usage. Dashboard Development & Business Intelligence Design and maintain interactive dashboards using Power BI, Tableau, or equivalent tools. Build KPI dashboards covering adoption, engagement, retention, content consumption, and campaign effectiveness. Create daily, weekly, and monthly reports for leadership and functional teams. Develop drill-downs by date, HCP segment, specialty, content type, feature, and campaign. Automate recurring reports and reduce manual Excel-based reporting. Standardize KPI definitions, calculation logic, reporting periods, and data sources. Implement data validation checks and optimize dashboard performance. Present trends, risks, opportunities, and recommended actions in a business-friendly format. Data Extraction, Transformation & Analysis Write SQL queries to extract, join, transform, aggregate, and analyze data from databases and analytical warehouses. Use Python and Pandas for data cleaning, transformation, exploratory analysis, and automation. Work with user events, campaign logs, content metadata, user profiles, and platform activity data. Build reusable analytical datasets, aggregations, and reporting tables. Perform exploratory analysis to identify trends, anomalies, correlations, and behavioural patterns. Optimize queries and workflows for performance and maintainability. Collaborate with Data Engineering and Backend Engineering on data availability and reliability. Support a consistent data model across product, content, and CRM reporting. Event Tracking & Data Quality Audit event tracking across critical platform journeys and features. Validate that required user actions and business events are captured correctly. Maintain an event dictionary with event names, definitions, properties, data types, and use cases. Identify missing or duplicate events, incorrect properties, timestamp inconsistencies, and identity-mapping issues. Validate identity resolution across application events, CRM systems, and user profiles. Monitor data freshness, completeness, consistency, and accuracy. Establish data quality checks and communicate tracking gaps to Engineering and Product. Document metric definitions and investigate unexpected changes in reports. Retention, Segmentation & Growth Analytics Develop HCP engagement segments based on activity, frequency, content preferences, and recency. Identify behavioural indicators associated with repeat usage and long-term retention. Analyze retention by acquisition cohort, specialty, feature adoption, and content consumption. Identify inactive users and quantify reactivation opportunities. Measure the contribution of features and content categories to overall engagement. Evaluate product releases, content initiatives, and CRM interventions. Recommend opportunities to improve activation, retention, and engagement depth. Partner with Product and CRM teams to define hypotheses and measure outcomes. Stakeholder Collaboration & Business Recommendations Work with Product, CRM, Marketing, Content, Engineering, and Leadership teams. Translate business questions into structured analytical problems. Present findings through clear visualizations, concise reports, and actionable recommendations. Participate in business reviews, KPI reviews, campaign reviews, and product performance discussions. Challenge assumptions with evidence and communicate analytical limitations. Maintain documentation for dashboards, data sources, metric definitions, and methodologies. Follow privacy, access control, and data governance practices for HCP data. Key Performance Indicators (KPIs) KPI Area Key Measurements Product Engagement DAU, WAU, MAU, sessions per user, engagement frequency User Retention D7/D30 retention, repeat visits, churn indicators, reactivation Feature Analytics Feature adoption, active users per feature, contribution to engagement Content Analytics Unique viewers, consumption, completion rate, repeat consumption, underutilized content Campaign Analytics Delivery, CTR, downstream engagement, campaign-attributed activity Data Quality Event coverage, completeness, duplicate events, identity-mapping accuracy Dashboard Delivery Reporting automation, freshness, reliability, stakeholder adoption Business Impact Insights implemented and measurable improvements attributable to interventions Required Technical Skills SQL and Data Analysis Strong SQL skills, including joins, CTEs, window functions, aggregations, subqueries, and query optimization. Experience analyzing large datasets and building reusable analytical queries. Understanding of data modelling, relational databases, and analytical reporting structures. Python Proficiency in Python for data analysis and automation. Hands-on experience with Pandas, NumPy, and data visualization libraries. Ability to clean, transform, aggregate, and validate large datasets. Familiarity with Jupyter Notebooks and reusable analytical scripts. Business Intelligence Practical experience with Power BI, Tableau, or equivalent tools. Ability to design KPI dashboards, interactive reports, drill-downs, and management scorecards. Understanding of data models, calculated measures, filters, and dashboard performance optimization. Product and Marketing Analytics Experience with CleverTap, Mixpanel, Amplitude, Firebase Analytics, or similar event-based analytics tools. Understanding of user journeys, funnels, cohorts, retention, campaign attribution, and behavioural segmentation. Familiarity with push notification and in-app campaign analytics. Data Infrastructure Understanding of cloud-based data storage and analytics platforms. Familiarity with AWS S3, Athena, Redshift, or equivalent technologies. Ability to work with CSV, Excel, JSON, event logs, and structured datasets. Basic understanding of ETL/ELT pipelines, APIs, scheduled jobs, and automated reporting. Required Qualifications Bachelor’s degree in Technology. 1 - 2 years of hands-on experience in Data Analytics, Product Analytics, Business Intelligence, or a similar role. Strong analytical thinking and problem-solving ability. Proven experience writing SQL queries and performing data analysis using Python or equivalent tools. Experience building dashboards and translating findings into business recommendations. Ability to independently manage analytical tasks from data extraction through insight delivery. Strong written and verbal communication skills. Equivalent practical experience may be considered in place of a specific academic background.