Data Engineer - R/Python

Description :

Responsibilities :

- Collect, clean, and analyze data from multiple sources to support business objectives.

- Build, validate, and deploy machine learning models to improve product features, customer experience, and operational efficiency.

- Work closely with cross-functional teams to design experiments, track KPIs, and measure the impact of data-driven initiatives.

- Maintain clear documentation, reproducible code, and dashboards to track model performance and business outcomes.

- Continuously explore new datasets, tools, and methodologies to improve analytics capabilities.

- Ensure data quality, integrity, and governance across all projects.

Top 3 Daily Tasks :

- Analyze large datasets to extract insights, identify patterns, and support decision-making.

- Develop predictive and prescriptive models using machine learning, statistical, and optimization techniques.

- Collaborate with Product, Engineering, and Business teams to implement data-driven solutions and track their impact.

Requirements :

- Someone with strong experience in data analysis, statistical modeling, and machine learning.

- A professional comfortable working with large, complex datasets and building actionable insights.

- Someone with hands-on experience in Python, R, SQL, and data visualization tools.

- Detail-oriented, organized, and passionate about building robust, scalable, and reproducible models.

- Excellent in communication, able to translate technical findings into business recommendations.

- Proactive and comfortably manage multiple analytics projects simultaneously while ensuring alignment across teams.

- 2-5 years of hands-on experience in data science or analytics roles.

- Proven experience in building and deploying machine learning models.

Top 5 Skills You Should Possess :

- Strong programming skills in Python (or R) and experience with libraries like Pandas, NumPy, scikit-learn, or TensorFlow.

- Proficiency in SQL and working with relational and non-relational databases.

- Solid understanding of statistics, hypothesis testing, and data modeling techniques.

- Experience with data visualization tools like Tableau, Power BI, or Matplotlib/Seaborn.

- Ability to work independently, prioritize tasks, and communicate findings clearly to non-technical stakeholders.