Data Science Engineer

Position Overview

The Data Scientist will analyze complex datasets to generate insights, build predictive models, and support data-driven decision-making across the organization. This role involves working with cross-functional teams to develop analytical solutions, improve business processes, and drive strategic initiatives through data science, statistical modeling, machine learning, and data visualization.

Key Responsibilities

1. Data Analysis & Modeling

• Collect, clean, and preprocess structured and unstructured data.
  
• Develop statistical models, machine learning algorithms, and predictive analytics solutions.
  
• Perform exploratory data analysis (EDA) to identify trends, patterns, and insights.
  

2. Machine Learning & AI Development

• Build, train, and optimize machine learning models for classification, regression, clustering, NLP, recommendation systems, etc.
  
• Monitor model performance and implement improvements or retraining as needed.
  
• Deploy models into production in collaboration with data engineering and ML engineering teams.
  

3. Data Visualization & Reporting

• Create dashboards, reports, and data visualizations to communicate insights to non-technical stakeholders.
  
• Present findings and recommendations in clear, concise, and actionable formats.
  

4. Collaboration & Business Partnership

• Work closely with product, engineering, marketing, finance, and operations teams to understand business problems and deliver data-driven solutions.
  
• Translate complex technical concepts into business-friendly insights.
  

5. Data Strategy & Innovation

• Contribute to the development of data science methodologies and best practices.
  
• Stay updated on emerging technologies, ML techniques, and industry trends.
  
• Support automation initiatives, model governance, and MLOps practices.
  

Qualifications & Experience

• Bachelors or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field.
  

• 2–6 years

  of experience in data science, analytics, or machine learning roles.
• Proficiency in

  Python

  or

  R

  , and experience with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Strong SQL skills and familiarity with data warehouses/lakes (e.g., BigQuery, Redshift, Snowflake).
  
• Experience with visualization tools such as Tableau, Power BI, or Python visualization libraries.
  

Key Skills

• Strong statistical and analytical thinking.
  
• Ability to work with large datasets and complex data pipelines.
  
• Solid understanding of machine learning algorithms and model evaluation.
  
• Excellent communication and storytelling skills.
  
• Problem-solving attitude and strong business acumen.