- Location
- USA
- Type
- Full-time
Description
Job brief
Collect, process, and perform statistical analyses of large datasets to generate actionable insights, identify trends, and support business decision-making using tools like SQL, Python, Excel, Power BI, or Tableau.
Requirements
Extract, clean, and transform raw data from multiple sources into structured formats suitable for analysis using ETL pipelines.
Interpret data, analyze results using statistical techniques, and provide ongoing reports to support business performance tracking.
Develop and implement data analyses, data collection systems, and other strategies that optimize statistical efficiency and quality using Python (pandas, NumPy) or R.
Work closely with management to prioritize business and information needs, aligning data models and dashboards with organizational KPIs.
Identify, analyze, and interpret trends or patterns in complex data sets using exploratory data analysis techniques and visualization tools.
Collaborate with cross-functional teams including IT, engineering, marketing, and finance to locate and define new process improvement opportunities.
Create dashboards, reports, and data visualizations to communicate trends, forecasts, and actionable insights to stakeholders via Tableau or Power BI.
Perform deep dive analysis into business problems and present clear, concise, and data-driven recommendations to non-technical audiences.
Develop and maintain databases, data systems – reorganizing data in a readable format, and ensuring data integrity through validation processes.
Utilize advanced Excel functions (pivot tables, VLOOKUP, macros) and SQL queries to retrieve and analyze data from relational databases.
Monitor key performance indicators and alert teams of anomalies using tools like Looker, Domo, or custom alerts.
Ensure data security and compliance with policies such as GDPR and HIPAA when handling sensitive information.
Document all analytics processes, query logic, and transformation methods for reusability and knowledge transfer.
Automate reporting and data extraction processes to improve efficiency and reduce manual tasks using Python, SQL scripts, or BI tools.
Perform A/B testing and statistical significance evaluation to support product or marketing decisions.
Contribute to data governance and quality initiatives by defining standards and participating in data audits.
Prepare technical documentation, write white papers, or create presentation decks to share key data stories with senior management.
Evaluate and recommend new tools or technologies for advanced data analytics, machine learning, or real-time data processing.
Support predictive modeling efforts by preparing training datasets and assisting data scientists in model evaluation.
Assist in building and maintaining data warehouses or data lakes on platforms like Snowflake, BigQuery, Redshift, or Azure Synapse.
Train team members or clients in the effective use of data tools, dashboards, and reports to promote data-driven culture.