Data Scientist

Role & responsibilities

Primary Skills

Statistical Analysis & Modeling: Proficiency in hypothesis testing, regression, clustering, and experimental design. Classical ML models for regression, classification, time series and anomaly detection
Programming: Expertise in Python for data manipulation, analysis, and modeling (e.g., Pandas, NumPy, scikit-learn).Machine Learning: Experience with supervised/unsupervised learning, NLP, and model deployment (e.g., TensorFlow, PyTorch).Data Wrangling: Cleaning, transforming, and structuring raw data using SQL, Spark, or similar tools.Data Visualization: Ability to communicate insights via tools like Tableau, Power BI, or Matplotlib/Seaborn.Big Data Technologies: Familiarity with Spark and cloud platforms (AWS, GCP, Azure).Communication: Translating technical findings into actionable business recommendations for non-technical stakeholders.

Secondary Skills

Deep Learning: Knowledge of neural networks, generative AI or agentic frameworks.
A/B Testing: Designing and analyzing results of experiments.Software Engineering: Understanding of CI/CD, APIs, and containerization (Docker, Kubernetes).