Lead Data Scientist

Company Description

Blend is looking for Lead Data Scientists 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.

Job Description

As a Lead 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.

What You'll Do

• 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.

Qualifications

Required Qualifications

• 5+ 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.

Preferred Qualifications

• 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.