Job title: Data Scientist

Location: [Company location]

Reports to: [Head of Data Science or Analytics Manager]

Employment type: [Full-time]

Job summary:

We're looking for a Data Scientist to turn [company name]'s data into models and insights that guide product and business decisions. The right person combines strong statistics with solid Python and SQL skills and explains findings in plain language.

Data scientists help the business make decisions based on evidence instead of guesswork. Their work improves forecasts, targets the right customers, and measures what changes actually work.

Key responsibilities:

• Write SQL to pull and join data from the data warehouse, such as [Snowflake, BigQuery, or Redshift].
• Clean, explore, and analyze large datasets in Python using pandas, NumPy, and Jupyter notebooks.
• Build and validate predictive models with scikit-learn, XGBoost, or similar libraries.
• Design and analyze A/B tests and other experiments, including sample size and significance checks.
• Build dashboards and reports in [Tableau, Looker, or Power BI] for business teams.
• Work with data engineers to define data pipelines, features, and data quality checks.
• Partner with ML engineers to move models into production and monitor their performance.
• Present findings and recommendations to product, marketing, and leadership teams.
• Document methods, assumptions, and code in Git so work can be reviewed and reused.
• Follow data privacy rules such as [GDPR or CCPA/CPRA] when handling personal data.

• Bachelor's degree in Statistics, Computer Science, Mathematics, or a related field; master's degree preferred.
• [X]+ years of experience in data science or advanced analytics.
• Strong Python and SQL skills.
• Solid grounding in statistics, regression, classification, and experiment design.
• Experience with a BI tool such as Tableau, Looker, or Power BI.
• Experience with Git and cloud data platforms.

• Statistical modeling.
• Machine learning.
• SQL and data wrangling.
• Experiment design.
• Data visualization.
• Clear communication with non-technical teams.

• Experience with causal inference or time-series forecasting.
• Experience using large language models (LLMs) for text analysis.
• Domain knowledge in [industry, e.g. e-commerce or fintech].

• [Health, dental, and vision insurance, or private medical cover]
• [Retirement plan, such as a 401(k), pension, or superannuation]
• [Paid time off and holidays]
• [Remote or hybrid work options]
• [Conference and training budget]
• [Salary range]