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.
As a Data Quality Analyst , you will be responsible for ensuring the accuracy, integrity, and reliability of our data across multiple systems and sources. You will collaborate closely with data engineers, analysts, and business teams to identify, analyze, and resolve data quality issues. Your work will directly support business operations and decision-making by maintaining high standards of data quality. Key Responsibilities: Data Validation: Verify data accuracy, completeness, and consistency across various sources and systems. Identify and resolve discrepancies and errors. Data Quality Assurance: Conduct rigorous quality checks on incoming and existing data to ensure it meets business and compliance requirements. Data Integrity: Ensure the integrity of data throughout its lifecycle, from ingestion to storage and reporting. Issue Identification & Resolution: Detect and troubleshoot data quality issues by conducting root cause analyses and working with cross-functional teams to implement solutions. Collaboration: Work with business, engineering, and data teams to continuously improve data quality and governance standards. Documentation: Maintain thorough documentation of data quality processes, issues, resolutions, and improvements. Reporting & Monitoring: Develop and manage data quality dashboards and reports to provide visibility into data quality metrics. Data Governance: Contribute to the development and enforcement of data governance policies and standards across the organization. Qualifications: Bachelor's degree. Strong attention to detail and accuracy in handling data. Familiarity with SQL and data profiling for data validation and analysis. Excellent problem-solving and analytical skills. Strong collaboration and communication abilities. Ability to work independently and manage deadlines. Understanding of data management principles, including data quality, governance, and integrity. Experience working with data quality tools and technologies or Snowflake is a plus Sample Scenario/Review for Data Validation: Review the following scenario and describe the steps you would take to ensure data quality: You are tasked with reviewing a set of customer order data for discrepancies and inconsistencies. ORDER TABLE : Order_ID, Customer_ID, Order_Date, Product_Code, Quantity, Address 2001, C001, 15/09/23, P001, 10, "123 Elm St, NY" 2002, C002, 18/09/23, P002, 5, 456 Maple Ave 2003, C003, 20/09/23, P003, NULL, "789 Oak Dr, CA" 2004, C001, "September 19, 2023", P001, 10, "123 Elm St, NY" 2001, C004, 21/09/23, P004, 7, NULL 2006, C005, N/A, P005, 2, "789 Oak Dr, CA" CUSTOMER TABLE : Customer_ID, Customer_Name C001, Alice Johnson C002, Bob Smith C003, Charlie Davis Order table with Order_ID as primary key (PK) and Customer table with Customer_ID as the primary key (PK) in the Customer table and as a foreign key (FK) in the Order table.
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