Data Scientist - Machine Learning & Statistical Modeling - IT

Role Overview:

We are looking for an experienced Data Scientist with strong expertise in machine learning, statistical modeling, and data analysis. The ideal candidate will be responsible for extracting meaningful insights from complex datasets, developing machine learning models, and delivering data-driven solutions to support business and product objectives.

Key Responsibilities:

• Collect, clean, preprocess, and analyze structured and unstructured datasets from multiple sources.
• Perform exploratory data analysis (EDA) to identify trends, patterns, correlations, and business insights.
• Develop, train, validate, and optimize machine learning models for various business use cases.
• Apply statistical techniques and predictive modeling approaches to solve complex problems.
• Perform feature engineering, feature selection, and model evaluation.
• Use appropriate metrics and validation techniques to assess model performance.
• Deploy and monitor machine learning models in production environments.
• Collaborate with data engineers, software engineers, product teams, and business stakeholders to translate business problems into analytical solutions.
• Develop data visualizations and dashboards to communicate insights effectively.
• Identify opportunities to improve existing models and analytical processes.
• Document data science workflows, methodologies, assumptions, and model results.
• Stay updated with emerging developments in machine learning, artificial intelligence, statistical modeling, and data science.

Required Skills & Experience:

• Strong experience in Data Science, Machine Learning, and Statistical Modeling.
• Proficiency in Python and commonly used data science libraries such as Pandas, NumPy, Scikit-learn, and SciPy.
• Strong understanding of machine learning algorithms, including regression, classification, clustering, and ensemble methods.
• Experience with exploratory data analysis, feature engineering, model development, and model evaluation.
• Strong knowledge of statistics, probability, and predictive modeling.
• Good proficiency in SQL for data extraction and analysis.
• Experience with data visualization tools such as Matplotlib, Seaborn, or Plotly.
• Experience deploying machine learning models using cloud platforms or production environments is an advantage.
• Strong analytical, problem-solving, and communication skills.