Data Scientist

Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch)

Working knowledge of statistical analysis and modeling

Experience with Jupyter Notebooks, RStudio, and data visualization tools

Familiarity with SQL and data querying

Machine Learning & AI

Solid understanding of machine learning algorithms (supervised & unsupervised)

Hands-on experience with: Regression (linear, logistic), Classification (decision trees, random forests, SVM), Clustering (K-means, hierarchical)

Experience with deep learning frameworks (TensorFlow, PyTorch) is a plus

Predictive Modeling

Proven experience in predictive modeling and forecasting

Ability to build, validate, and deploy predictive models

Strong understanding of:

Feature engineering

Model evaluation techniques (ROC, precision/recall, cross-validation)

Experience working with real-world datasets to derive actionable insights

Statistics & Data Analysis

Strong foundation in statistics and probability

Hypothesis testing, regression analysis, and statistical modeling

Data cleaning, transformation, and exploratory data analysis (EDA)

Data & Deployment (Preferred)

Experience with cloud platforms (AWS, Azure, or GCP)

Familiarity with Docker/containers is a plus

Exposure to MLOps practices (CI/CD for ML models)

Soft Skills

Strong analytical and problem-solving skills

Ability to translate business problems into data solutions

Effective communication and storytelling with data

Collaborative mindset with cross-functional teams

Nice-to-Have

Experience with big data tools (Spark, Hadoop)

Exposure to NLP, computer vision, or time-series forecasting

Knowledge of model deployment APIs (Flask, FastAPI)