We are seeking a Data Scientist with strong analytical judgment and hands-on experience solving business problems through data. You will work across diverse datasets and industries to investigate patterns, develop statistical and predictive approaches, and turn complex findings into practical recommendations. The role combines rigorous analysis, reliable implementation, and clear communication.

Key responsibilities

- Translate business questions into well-defined analytical problems, with explicit objectives, assumptions, success measures, and practical constraints.
- Explore, clean, and combine data from multiple sources; investigate missing values, duplicates, inconsistent records, outliers, and data-quality issues.
- Apply statistical analysis, hypothesis testing, regression, classification, forecasting, and anomaly detection as appropriate to the problem.
- Build and improve Python and SQL workflows for data preparation, feature engineering, analysis, and predictive modeling.
- Design sound validation approaches; identify data leakage, sampling bias, overfitting, and misleading metrics, and assess uncertainty and calibration where relevant.
- Independently verify calculations, investigate inconsistencies in analytical outputs, and test alternative assumptions and methods to improve reliability.
- Communicate findings, limitations, and recommendations through clear reports and reproducible code; collaborate with business and engineering teams to address issues.

Required skills and experience

- 3+ years of practical experience in data science, applied statistics,



machine learning, or quantitative analytics, supported by examples of real business work.
- Proficiency in Python and SQL, including complex joins, aggregations, debugging, and analysis of structured datasets.
- Hands-on familiarity with pandas, NumPy, scikit-learn, and statistical libraries such as SciPy or statsmodels.- Solid grounding in probability, statistical inference, experimental design, feature engineering, validation, and performance metrics.
- Ability to read and challenge existing analytical code, choose suitable methods, and explain why an approach is appropriate for the business context.
- Clear written communication, attention to detail, and the ability to work independently while documenting evidence and assumptions.
- A degree in statistics, mathematics, computer science, economics, engineering, or a related field, or equivalent professional experience.

Additional experience valued

Time-series analysis, imbalanced classification, geospatial analytics, entity matching and deduplication, financial reconciliation, or data-quality audits. Familiarity with Git, command-line tools, and reproducible analytical environments is helpful.

What success looks like

Accurate, reproducible analyses that answer the business question; dependable data workflows; well-supported recommendations; and clear explanations of uncertainty and limitations. We value independent problem-solving, sound judgment, and the ability to identify and resolve issues beyond what standard metrics alone reveal.

📌 Data Scientist (India)
🏢 Zyka AI
📍 India