Saurabh Kumar

Work History

Novartis Healthcare Private Limited

01.2023 - Current

Lead end-to-end data science projects focused on patient safety surveillance using large-scale real-world datasets, including Claims, Electronic Health Records (EHR), and Registries, primarily in the post-market phase of pharmaceutical products.
Design and implement advanced analytical pipelines and predictive models to detect and monitor safety signals, ensuring compliance with regulatory standards (e.g., FDA).
Apply statistical modeling, machine learning techniques, and rigorous data validation methods to extract insights from complex, longitudinal healthcare data.
Collaborate with cross-functional teams (clinical, regulatory, and business stakeholders) to translate business objectives into analytical frameworks and ensure alignment with Statistical Analysis Plans (SAPs).
Mentor and lead teams of analysts and data scientists, providing guidance on model development, data engineering best practices, and domain-driven analytics.
Contributed to innovation through participation in internal hackathons and data challenges, earning multiple awards for impactful, data-driven solutions in the RWE space.
Drive continuous improvement by integrating modern data science techniques and automation tools into traditional analytics workflows to enhance scalability and reproducibility.

Pharm Easy

12.2021 - 01.2023

Spearhead end-to-end analytics for supply chain optimization, partnering with product managers and warehouse operations to integrate data-driven insights into sales forecasting, inventory management, and procurement strategy.
Utilize statistical analysis, predictive modeling, and data mining techniques to identify inefficiencies, forecast demand, and reduce stockouts, enhancing supply chain resilience and responsiveness.
Lead a team of 8 analysts, providing technical mentorship in data analysis, ML model development, and performance tracking; align team efforts with key business KPIs and strategic goals.
Build and deploy analytics solutions that drive operational efficiency, including dashboards, anomaly detection systems, and automated reporting tools.
Present actionable insights and predictive analytics directly to senior leadership (Country Head), supporting evidence-based decision-making on product lifecycle and logistics planning.
Successfully designed and implemented a machine learning–driven supply chain pipeline that reduced null search results, improved stock availability, and increased procurement efficiency—contributing to a $3M revenue lift in six months.
Tools & Technologies: Python, SQL, pandas, scikit-learn, Power BI/Tableau, time series forecasting, A/B testing, supply chain analytics.

Eli Lilly Service India Limited

08.2020 - 12.2021

Led data science initiatives to optimize clinical trial site selection by developing predictive models and location-based analytics that evaluated site performance, patient availability, and cost-efficiency across U.S. and global trial locations.
Applied machine learning algorithms and statistical techniques to streamline the trial feasibility process, significantly reducing operational overhead and accelerating site activation timelines.
Utilized real-world datasets (e.g., EHR, claims) to identify high-potential recruitment zones and inform strategic decision-making for trial deployment and resource allocation.
Played a key role in pilot RWE projects, designing analytics frameworks to assess patient populations, treatment pathways, and outcomes—projects that were later scaled into long-term initiatives due to demonstrated business value.
Recognized with multiple internal awards for innovative use of real-world data to enhance trial design, improve site targeting accuracy, and contribute to the organization's data-driven transformation in clinical operations.
Tools & Techniques: Python, SQL, scikit-learn, pandas, logistic regression, clustering, geospatial analysis, clinical data modeling, real-world data integration.

Vodafone Shared Service

12.2016 - 08.2020

Delivered end-to-end data science solutions across Consumer and Enterprise business verticals spanning multiple regions, applying statistical modeling and machine learning techniques to solve high-impact problems such as sales forecasting, campaign performance analysis, churn prediction, and customer inactivity modeling.
Built and deployed predictive models (e.g., logistic regression, decision trees, time series forecasting) to identify at-risk customers, forecast sales trends, and optimize marketing strategies for improved ROI.
Translated complex data into actionable business insights using exploratory data analysis, data visualization, and storytelling to support strategic decision-making and operational improvements.
Collaborated with cross-functional teams—including product, marketing, and engineering—to ensure smooth execution of analytics initiatives and alignment with business objectives.
Spearheaded internal projects focused on automation, KPI tracking, and process optimization, contributing to measurable gains in operational efficiency.
Mentored junior analysts and data scientists, fostering a culture of continuous learning and analytical excellence.
Consistently recognized with awards for innovation, impact, and delivery of scalable, high-value analytics solutions in fast-paced, data-rich environments.
Tools & Techniques: Python, SQL, scikit-learn, XGBoost, Tableau/Power BI, A/B testing, segmentation, time series analysis, data pipelines.

Infosys Ltd

02.2014 - 12.2016

Developed and deployed advanced machine learning models in the Retail and BFSI (Banking, Financial Services, and Insurance) sectors, delivering high-impact solutions in marketing optimization and fraud detection.
Applied supervised and unsupervised learning techniques (e.g., classification, clustering, anomaly detection) to solve complex business problems such as customer segmentation, campaign targeting, and fraud risk mitigation.
Built data pipelines and predictive models to analyze transactional patterns, detect fraudulent behavior, and improve targeting strategies, resulting in enhanced security and improved marketing ROI.
Continuously upskilled in modern data science tools and technologies, demonstrating adaptability and a strong commitment to staying ahead in a rapidly evolving data landscape.
Collaborated with cross-functional teams across product, compliance, and analytics departments to align data-driven initiatives with strategic business goals.
Recognized with multiple awards for delivering innovative, data-backed solutions and driving measurable business impact in fast-paced, high-stakes environments.
Tools & Techniques: Python, SQL, scikit-learn, pandas, NumPy, Tableau, anomaly detection, logistic regression, decision trees, data wrangling, feature engineering.