Experience
4 - 5 yrs
Job Location
Bengaluru, India
Vacancy
1
Designation
Data Scientist
Job Type
Not specified
Job Description
Hiring for Data Scientist - Bangalore About the Role: We are seeking a talented and detail-oriented Data Scientist to join our growing team. In this role, you will analyze large datasets, develop predictive models, and collaborate across departments to drive data-informed decision-making. You will play a key role in shaping product strategies, customer experiences, and operational efficiencies through data insights. Responsibilities:
- Collect, clean, and preprocess structured and unstructured data from various sources.
- Develop and deploy machine learning models and algorithms for predictive and prescriptive analytics.
- Create dashboards and visualizations to communicate findings to stakeholders.
- Collaborate with cross-functional teams (engineering, product, marketing, finance) to understand business needs and translate them into analytical solutions.
- Conduct A/B testing and statistical analysis to evaluate model performance and business outcomes.
- Identify data trends, patterns, and opportunities for improvement and automation.
- Document processes, models, and data pipelines to ensure reproducibility and transparency.
- Bachelors or Masters degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Proficiency in programming languages such as Python or R .
- Strong knowledge of SQL and experience working with relational databases.
- Hands-on experience with machine learning libraries (e.g., scikit-learn , TensorFlow , PyTorch ).
- Familiarity with data visualization tools like Tableau , Power BI , or Plotly .
- Solid understanding of statistical methods, experimental design, and data modeling.
- Excellent problem-solving and communication skills.
- Experience with cloud platforms (e.g., AWS, GCP, Azure).
- Knowledge of big data tools (e.g., Spark, Hadoop).
- Background in a specific industry (e.g., fintech, healthcare, e-commerce) is a plus.
- Experience in MLOps and model deployment in production environments.
No Referrers Available
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