Data Scientist

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.

Minimum Work Experience Required:

• 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.