Data Scientist - Machine Learning / Statistical Modeling

Job Description Data Scientist (Machine Learning & Statistical Modeling)

Company:

Job Summary

Aaum Analytics is looking for a Data Scientist with 3–5 years of experience in machine learning, statistical modeling, and predictive analytics. The ideal candidate should have strong Python programming skills and practical experience in developing, evaluating, and optimizing machine learning and statistical models to solve real-world business problems.

Key Responsibilities

• Develop, train, validate, and optimize machine learning and statistical models.
• Perform data cleaning, preprocessing, exploratory data analysis, and feature engineering.
• Build predictive models using regression, classification, clustering, and time-series techniques.
• Apply statistical methods, hypothesis testing, and probability concepts to analyze data and validate model performance.
• Evaluate models using appropriate metrics and perform hyperparameter tuning.
• Work with Python libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib.
• Apply machine learning and deep learning techniques to business use cases.
• Develop and integrate model-serving APIs using FastAPI or Flask.
• Work with SQL databases and data processing pipelines.
• Deploy models and monitor their performance in production environments.
• Collaborate with engineering and business teams to develop data-driven solutions.
• Maintain technical documentation and follow version control and model development best practices.

Required Skills

• 3–5 years of relevant experience in Data Science, Machine Learning, or Statistical Modeling.
• Strong proficiency in Python programming.
• Solid understanding of statistics, probability, and machine learning algorithms.
• Hands-on experience with regression, classification, clustering, and predictive modeling.
• Experience in feature engineering, model evaluation, validation, and hyperparameter tuning.
• Proficiency in Pandas, NumPy, and Scikit-learn.
• Experience with SQL and data manipulation.
• Knowledge of statistical testing, model performance metrics, and data interpretation.
• Familiarity with Git and software development practices.

Preferred Skills

• Experience with XGBoost, Random Forest, Gradient Boosting, and time-series forecasting.
• Knowledge of TensorFlow or PyTorch and deep learning techniques.
• Exposure to NLP, Generative AI, LLMs, prompt engineering, and RAG.
• Familiarity with FastAPI, Flask, Docker, MLflow, and MLOps.
• Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.

Preferred Candidate Profile

Candidates should have hands-on experience developing and validating ML or statistical models, a strong analytical mindset, and the ability to translate business problems into data-driven solutions.

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