We are seeking an inspiring, technically savvy, data scientist who is passionate about building a best-in-class experimentation platform/program to support our rapidly growing suite of eCommerce products.
As a Senior Data Scientist, you will be a key player in driving data-driven decision-making across the organization, collaborating closely with engineering, product, marketing, and other cross-functional teams to deliver insights and products that shape the future of our business. You will also mentor junior data scientists and help to foster a culture of experimentation throughout the organization.
Roles & Responsibilities:
Core Responsibilities:
Data Pipeline Development and Optimization:
- Design, develop, and maintain robust data pipelines to ensure efficient and accurate data flow from various sources to the data warehouse.
- Ensure the integrity and consistency of experimental data, enabling accurate analysis and reliable insights to drive decision-making and optimize business strategies.
Experimentation Design & Analysis:
- Support the design and execution of A/B tests, multivariate experiments, and randomized controlled trials (RCTs) to assess the impact of product changes, marketing campaigns, and customer experiences.
- Develop and implement robust methodologies to measure the effectiveness of business initiatives (e.g., website features, promotions, UI changes, etc.) using experimentation frameworks.
- Own the end-to-end experimentation pipeline, including hypothesis generation, experimental design, implementation, monitoring, and post-experiment analysis.
- Identify and mitigate biases in experiment design and results, ensuring statistical rigor and reliability.
Advanced Statistical Analysis & Modelling:
- Conduct advanced statistical analysis (e.g., causal inference, Bayesian analysis, regression modelling) to derive actionable insights from experimentation results.
- Develop and refine models to predict customer behavior and optimize conversion rates, retention, and other key business metrics.
- Analyze large-scale datasets and design efficient algorithms to support decision-making in areas like pricing, product recommendations, and personalization.
Continuous Improvement & Innovation:
- Stay current with the latest advancements in data science, statistics, and experimentation methodologies.
- Propose innovative approaches to enhance the experimentation framework, such as new experimental designs, alternative modelling techniques, or improved metrics.
- Lead or participate in research to explore new ways of measuring and optimizing the customer journey in a retail/e-commerce setting.
Years of Experience:
- 5+ years of professional experience in data science, with at least 2 years focused on experimentation, A/B testing, and causal inference in a retail or e-commerce environment.
- Proven track record of designing and analyzing large-scale A/B tests and experiments with demonstrable business impact.
- Strong experience with statistical analysis and modelling techniques, including hypothesis testing, regression analysis, and Bayesian statistics
Education Qualification & Certifications (optional)
Required Minimum Qualifications :
- Ph.D. or master s degree in data science, Statistics, Mathematics, Computer Science, Economics, or a related field.
Skill Set Required
- Advanced knowledge of statistical methodologies for experiment design, analysis, and causal inference.
- Expertise in analytics/data software/tools such as Python, R, SQL, and experience with machine learning frameworks (e.g., TensorFlow, scikit-learn).
- Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders and executive leadership.
- Solid understanding of e-commerce and retail metrics (e.g., conversion rate, customer lifetime value, churn, etc.) and how they relate to experimentation.
Secondary Skills (desired)
- Experience with large-scale e-commerce platforms and digital product development.
- Familiarity with the advanced causal and inferential analytics
- Experience with advanced techniques in machine learning or AI that complement experimentation (e.g., recommender systems, predictive modelling).
- Familiarity with cloud-based platforms (e.g., AWS, Google Cloud, Azure).
- Experience working in an agile environment and collaborating with cross-functional teams in a fast-paced business setting.
