Data Scientist – Python, ML & Predictive Modeling

Data Scientist – Python, ML & Predictive Modeling

Duration: 12 months

Work Model: 100% Remote

Programming & Tools

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 supervised and unsupervised machine learning algorithms.

Hands-on experience with:

Regression (Linear, Logistic)

Classification (Decision Trees, Random Forests, SVM)

Clustering (K-Means, Hierarchical)

Experience with deep learning frameworks such as TensorFlow and 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.

Experience with hypothesis testing, regression analysis, and statistical modeling.

Proficiency in data cleaning, transformation, and exploratory data analysis (EDA).

Data & Deployment (Preferred)

Experience with cloud platforms such as AWS, Azure, or GCP.

Familiarity with Docker and containerization is a plus.

Exposure to MLOps practices and CI/CD for machine learning models.

Soft Skills

Strong analytical and problem-solving skills.

Ability to translate business problems into data-driven solutions.

Effective communication and storytelling with data.

Collaborative mindset with cross-functional teams.

Nice-to-Have

Experience with big data technologies such as Spark and Hadoop.

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

Knowledge of model deployment APIs such as Flask and FastAPI.