Sr Data Scientist

Blend is looking for a Senior Data Scientist to join a high-impact Data Science and analytics engagement with a leading organization.

This role is suited for a hands-on Data Scientist who combines strong Machine Learning and statistical foundations with experience working on customer, marketing, campaign, and business analytics problems .

You will work closely with Data Scientists, Data Engineers, business stakeholders, and cross-functional teams to develop scalable analytical solutions and translate complex data into actionable business outcomes.

As a Senior Data Scientist, you will develop and deploy machine learning and advanced analytics solutions using large-scale customer and business datasets.

The role requires strong hands-on experience with Python, SQL, Databricks, and PySpark , along with a solid understanding of Machine Learning and statistical modeling.

You will work on problems related to customer behavior, campaign effectiveness, targeting, response modeling, and business performance , helping stakeholders make better data-driven decisions.

Responsibilities

Develop and implement Machine Learning models to solve complex business and customer analytics problems.; Build predictive models for customer behavior, campaign response, targeting, propensity, and other business outcomes.; Perform feature engineering, model development, validation, tuning, and performance evaluation.; Work with large and complex datasets using Databricks and PySpark .; Write efficient and scalable SQL for data extraction, transformation, aggregation, and analysis.; Use Python and relevant Data Science libraries to develop analytical solutions.; Analyze customer and campaign data to identify behavioral patterns, trends, opportunities, and areas for improvement.; Support campaign analytics , including campaign performance measurement, customer response analysis, targeting, and effectiveness assessment.; Translate business and marketing questions into appropriate Data Science methodologies.; Apply statistical techniques and Machine Learning approaches to identify meaningful customer and business insights.; Work closely with Data Engineers to prepare and leverage scalable data pipelines and analytical datasets.; Validate models and analytical approaches using appropriate statistical and Machine Learning evaluation techniques.; Communicate analytical findings, model results, and recommendations clearly to technical and non-technical stakeholders.; Partner with business teams to convert analytical insights into measurable business actions and outcomes.; Contribute to productionizing Data Science solutions and following best practices around code quality, version control, testing, and model lifecycle management.; Mentor junior Data Scientists and contribute to the broader technical capability of the team.

Requirements

4+ years of professional experience in Data Science / Machine Learning / Advanced Analytics.; Strong hands-on programming experience in Python .; Strong hands-on SQL skills, including complex joins, aggregations, transformations, and analysis of large datasets.; Mandatory hands-on experience with Databricks.; Strong experience with PySpark / Apache Spark and distributed data processing.; Strong foundation in Machine Learning and predictive modeling.; Hands-on experience with:; Classification; Regression; Feature engineering; Model selection; Model validation; Hyperparameter tuning; Model evaluation; Strong understanding of statistics and applied statistical modeling.; Experience working with large-scale datasets in an enterprise environment.; Experience applying Data Science to customer, marketing, campaign, or business analytics problems .; Experience analyzing campaign performance, customer response, targeting, propensity, or marketing effectiveness.; Strong ability to translate business problems into analytical solutions.; Ability to communicate technical concepts and analytical findings to business stakeholders.

Nice to have

Experience in customer analytics, marketing analytics, CRM, loyalty, retail, consumer, or other customer-centric domains .; Experience with propensity, response, churn, conversion, or targeting models.; Experience with customer segmentation and behavioral analytics.; Experience working with Databricks-based Data Science environments.; Experience with cloud platforms such as AWS, Azure, or GCP.; Experience with MLflow or similar model lifecycle/experiment tracking platforms.; Familiarity with data visualization and communicating insights through dashboards and presentations.; Experience working in Agile / cross-functional Data Science teams.; Master's degree in Data Science, Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.