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Data Scientist
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Key Responsibilities
Data Analysis & Modeling
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).
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Apply advanced statistical techniques and data mining methods to uncover patterns, insights, and opportunities.
- 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.
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Communicate complex analytical concepts clearly to non‑technical audiences.
- Collaborate with Engineering teams to productionize models and ensure reliable deployment of analytics solutions.
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Monitor model performance, conduct error analysis, and iterate to improve accuracy and robustness.
- Provide technical guidance and mentorship to junior data scientists and analysts.
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Partner with cross‑functional teams to integrate data science capabilities into product workflows and strategic planning.
- Contribute to the development of scalable data pipelines and analytics tools.
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Promote best practices in data quality, documentation, and reproducibility.
- 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.
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