Data Scientist

Key Responsibilities

Data Analysis & Modeling

• Collect, clean, and preprocess large structured and unstructured datasets.
• Develop and validate predictive models, machine learning algorithms, and statistical frameworks that solve business problems (e.g., churn prediction, user segmentation, personalization).
• Apply advanced statistical techniques and data mining methods to uncover patterns, insights, and opportunities.

Business Insight & Strategy

• Translate analytical results into actionable business recommendations for stakeholders.
• Work closely with product and marketing teams to define KPIs and measurement frameworks that drive performance. 
• Communicate complex analytical concepts clearly to non‑technical audiences.

Model Deployment & Impact

• Collaborate with Engineering teams to productionize models and ensure reliable deployment of analytics solutions.
• Monitor model performance, conduct error analysis, and iterate to improve accuracy and robustness.

Mentorship, Leadership & Collaboration

• Provide technical guidance and mentorship to junior data scientists and analysts.
• Partner with cross‑functional teams to integrate data science capabilities into product workflows and strategic planning.

Data Infrastructure & Best Practices

• Contribute to the development of scalable data pipelines and analytics tools.
• Promote best practices in data quality, documentation, and reproducibility.

Qualifications & Skills

• 5+ years of professional experience in data science, analytics, or a related role.
• Strong programming skills in Python, R, or similar languages for data analysis and modeling.
• Expertise with data manipulation and querying languages such as SQL.
• Hands‑on experience with statistical modeling, machine learning techniques (classification, regression, clustering, etc.), and predictive analytics.
• Familiarity with data visualization tools like Tableau, Power BI, Matplotlib, or Seaborn to communicate insights.
• Experience with cloud platforms and big data technologies (e.g., AWS, GCP, Hadoop, Spark) is a plus.
• Strong analytical, problem‑solving, and critical‑thinking skills.
• Excellent communication skills with the ability to collaborate with both technical and business stakeholders.
• Advanced degree (Master’s or PhD) in Data Science, Statistics, Computer Science, Mathematics, or a related discipline is desirable but not mandatory.