Data Scientist

Mu Sigma; Bengaluru; Sur place; INR 1 500 000 - 1 900 000; Plein temps

Résumé du poste

Mu Sigma in Bengaluru seeks an experienced data scientist to build predictive and descriptive models for pharma and life sciences commercial analytics. The role analyzes large healthcare datasets to uncover business insights and supports sales, marketing, and market performance measurement.

You will design data pipelines, perform feature engineering, and validate models while collaborating with data engineering and consulting teams. Strong Python/R, SQL, and BI skills are required.

Qualifications

• Bachelor's or Master's degree in a quantitative field.
• 2-7 years of data science experience in pharma/life sciences analytics.
• Understanding of pharma commercial processes and performance measurement.
• Hands-on experience with ML algorithms and predictive modeling.
• Proficiency in Python or R for analysis and modeling.
• Strong SQL skills for data extraction and manipulation.
• Experience with Tableau/Power BI for dashboards.
• Familiarity with cloud platforms (AWS/Azure/GCP) is preferred.
• Knowledge of Agile methodologies and Git is an advantage.

Responsabilités

• Develop predictive and descriptive analytical models for commercial objectives.
• Analyze large healthcare, pharma, sales, claims data for insights.
• Build models for customer segmentation, forecasting, next-best action.
• Design data pipelines, feature engineering, and model validation.
• Collaborate with stakeholders to translate business problems into analytics.
• Create dashboards and visualizations to communicate findings.
• Monitor model performance and suggest improvements.
• Work with cross-functional teams including data engineering and consulting.
• Ensure data quality and governance compliance.

Connaissances

Python

SQL

R (preferred)

Machine Learning

Statistical Modeling

Data Visualization

Spark or PySpark (preferred)

Databricks (preferred)

Cloud Platforms (AWS/Azure/GCP)

Formation

Bachelor's or Master's in data science / statistics / CS / mathematics / engineering

Outils

Tableau

Power BI

Spark

PySpark

Databricks

AWS

Azure

GCP

Description du poste

Responsibilities:

• Develop predictive and descriptive analytical models to support commercial business objectives.
• Analyze large-scale healthcare, pharmaceutical, sales, claims, prescription (Rx), patient, and market data to uncover business insights.
• Build machine learning and statistical models for customer segmentation, targeting, forecasting, next best action, and campaign effectiveness.
• Design and implement data pipelines, feature engineering, and model validation processes.
• Collaborate with commercial stakeholders to translate business problems into analytical solutions.
• Create dashboards and visualizations to communicate findings effectively using BI tools.
• Monitor model performance and recommend improvements based on business feedback.
• Work with cross-functional teams including data engineering, business consulting, and commercial excellence teams.
• Ensure data quality, documentation, and adherence to data governance standards.

Requirements:

• Bachelor's or master's degree in data science, statistics, computer science, mathematics, engineering, or a related quantitative discipline.
• 2-7 years of experience in data science with a focus on pharma or life sciences commercial analytics.
• Strong understanding of pharmaceutical commercial processes, including sales analytics, marketing analytics, customer analytics, and market performance measurement.
• Hands-on experience with machine learning algorithms, statistical analysis, and predictive modeling.
• Proficiency in Python or R for data analysis and model development.
• Strong SQL skills for data extraction and manipulation.
• Experience with data visualization tools such as Tableau or Power BI.
• Familiarity with cloud platforms (AWS, Azure, or GCP) is preferred.
• Understanding of Agile methodologies and version control tools such as Git is an advantage.
• Technical Skills: Python, SQL, R (preferred), Machine Learning, Statistical Modeling, Data Visualization (Tableau/Power BI), Spark or PySpark (preferred), Databricks (preferred), Cloud Platforms (AWS, Azure, or GCP).

Soft Skills:

• Strong analytical and problem-solving abilities.
• Excellent communication and stakeholder management skills.
• Ability to present complex analytical findings to non-technical audiences.
• Strong collaboration and teamwork skills.
• Self-driven with the ability to manage multiple priorities.