Senior Data Scientist

Responsibilities
Design, develop, and deploy predictive classification and regression models, along with anomaly detection models and algorithms.
Conduct extensive EDA, feature engineering, and data preprocessing to ensure high-quality input for ML models.
Evaluate and optimize model performance using statistical and ML techniques.
Design and execute A/B tests to measure and validate model impact.
Perform customer segmentation using various clustering techniques.
Develop and implement model monitoring dashboards and establish model governance techniques.
Collaborate with Analytics, Product, Engineering, and Marketing teams to seamlessly integrate predictive models into workflows.
Work with the ML Engineering team to ensure efficient data pipelines and scalable model deployment.
Analyze diverse datasets to extract meaningful insights and patterns, identifying actionable opportunities for optimization and innovation.

Qualifications
About You

5+ years of experience in data science with a strong emphasis on machine learning modeling.
Proficiency in Python for data analysis and ML, with experience using libraries such as Scikit-Learn, XGBoost, TensorFlow, or PyTorch.
Expertise in SQL and working with large, complex structured and semi-structured datasets.
Strong understanding of core machine learning techniques, including logistic regression, gradient boosting, decision trees, and clustering methods.
Experience in feature engineering, model selection, and performance optimization.
Experienced in designing, executing, analyzing, and reporting on experiments.
Strong communication skills and ability to present findings to technical and non-technical stakeholders.
Master’s or higher degree in Data Science, Computer Science, Statistics, or a related field is preferred.

Preferred Skills & Qualifications

Experience working in fintech, e-commerce, or other data-rich consumer-facing industries.
Familiarity with Google Cloud Platform (GCP) services, particularly Vertex AI and Dataflow, for scalable data processing and model training.
Experience with dbt for data modeling.
Familiarity with BigQuery or other MPP (Massively Parallel Processing) databases.
Experience using the Scala programming language for developing scalable and efficient data pipelines.