Key Responsibilities Causal Inference & Experimentation Design and analyze experiments (A/B tests, observational studies) to identify causal relationships between variables. Apply advanced causal inference methods (e.g., propensity score matching, difference-in-differences, instrumental variables, DAGs) to estimate treatment effects. Use tools like DoWhy , CausalML , or EconML to model causal relationships. Forecasting & Multivariate Analysis Develop time-series forecasting models (e.g., ARIMA, Prophet, LSTM) to predict business metrics (sales, demand, churn). Perform multivariate regression analysis to identify key drivers of outcomes. Validate model accuracy and refine approaches based on feedback. Data Analysis & Visualization Clean, preprocess, and analyze large datasets to extract meaningful patterns. Build dashboards and reports to communicate findings to stakeholders using tools like Tableau, Power BI, or Python (Matplotlib/Seaborn). Collaboration Partner with product, marketing, and operations teams to define KPIs and design data-driven strategies. Translate complex technical results into actionable business recommendations. Technical Skills : Causal Inference : Proficiency in methods like propensity score matching, synthetic controls, and counterfactual analysis. Forecasting : Experience with time-series models (Prophet, ARIMA) and machine learning frameworks (Scikit-learn, TensorFlow). Programming : Strong Python/R skills (Pandas, NumPy, StatsModels, CausalInference). Data Wrangling : SQL proficiency for querying large datasets. Experience : 5+ years in data science, with a focus on causal inference and experimental design. Experience in industries like e-commerce, retail, or tech is a plus.

Responsibilities Design and implement scalable and efficient data pipelines using dbt and Snowflake. Work collaboratively within a diverse team to spearhead the migration of data from multiple ERPs and SQL Server systems, using Extract and Load tools. Apply your technical expertise to ensure efficient and accurate data integration. Leverage your skills to maintain and enhance existing legacy systems and reports. Engage in reverse engineering to understand these systems and incrementally improve them by applying patches, optimizing functionality, and transitioning data pipelines to the Modern Data Platform (MDP). Practice clean programming techniques, write self-documenting code, and manage the codebase using GIT version control. Contribute to automation efforts by implementing CI/CD pipelines to streamline deployments. Work closely with onshore and offshore team members, as well as global stakeholders, to promote effective teamwork and a solution-oriented mindset. Tackle technical challenges with a 'we got this' mentality to achieve shared goals. Play an active role in continuously improving data integration processes, orchestrating workflows for maximum efficiency and reliability. Preferred candidate profile Experience: 5+ years of experience working with data integration and transformation, including a strong understanding of SQL for data querying and manipulation. Technical Skills: Must have: Cloud Data Warehousing Exposure: Experience with Snowflake or comparable cloud based data systems and tools. Proficiency in Python and SQL. Strong adherence to clean programming practices, producing self-documenting code using coding best practices. Hands-on experience with CI/CD tools (e.g., Jenkins, Github CI, CircleCI). Nice to have: Experience implementing and orchestrating data integrations Experience with containerization and orchestration tools (e.g., Docker, Kubernetes). Familiarity with infrastructure as code tools (e.g., Terraform, CloudFormation). Familiarity with configuration management tools (e.g., Ansible, Puppet, Chef). Knowledge of cloud platforms (AWS, Azure, GCP). Technical Proficiency and Problem-Solving: Deep understanding of data integration tools and methods, coupled with a proven ability to troubleshoot complex technical challenges. Communication and Agile Experience: Excellent communication skills for translating technical concepts to non-technical stakeholders, with comfort in Agile methodologies and project management tools.