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Open nowPosted 8 hours ago

Data Scientist

Workable (global search)107,903 open roles

Where
Bengaluru, KA, India
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Your applicationOpen nowData ScientistWorkable (global search) · Bengaluru, KA, India

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Early applications get read.

10.5% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 6 days.

Share of postings closed within
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  2. 4.3%3 days
  3. 10.5%7 days
  4. 15.9%14 days
  5. 34.1%30 days
This job: posted 8 hours ago

Workable (global search) median: 6 days open

The posting

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: ₹𝟱𝟱 𝗟𝗣𝗔

Experience: 2+ yrs

Location: Bengaluru

We are looking for a talented and analytical Data Scientist to develop data-driven solutions, build predictive models, and translate complex datasets into actionable business insights. The ideal candidate will have strong foundations in statistics, machine learning, programming, and data analysis, along with the ability to solve real-world business problems using advanced analytical techniques.

This role offers the opportunity to work on meaningful data science initiatives, collaborate with cross-functional teams, and contribute to solutions that improve decision-making, operational efficiency, and business performance.

Requirements

Key Responsibilities

  • Analyse structured and unstructured datasets to identify patterns, trends, correlations, and business opportunities.
  • Perform exploratory data analysis, data cleaning, transformation, and feature engineering to prepare datasets for modelling.
  • Develop, train, evaluate, and optimise machine learning models for predictive analytics and business problem-solving.
  • Apply statistical techniques and quantitative methods to support forecasting, classification, segmentation, and other analytical use cases.
  • Translate business requirements into well-defined data science problems and measurable model objectives.
  • Build reusable analytical workflows and maintain high standards of code quality, reproducibility, and documentation.
  • Evaluate model performance using appropriate statistical metrics, validation techniques, and testing methodologies.
  • Collaborate with data engineers and software developers to integrate models into applications, pipelines, and production environments.
  • Monitor model performance, investigate inaccuracies, and recommend improvements to maintain reliability.
  • Communicate insights, model results, assumptions, and recommendations to technical and non-technical stakeholders.
  • Create visualisations, reports, and analytical summaries to support data-driven decision-making.
  • Identify opportunities to automate repetitive analytical tasks and improve data science workflows.
  • Stay updated on emerging machine learning techniques, analytical tools, and industry best practices.

What Makes You a Great Fit

  • 2–5 years of relevant experience in data science, machine learning, advanced analytics, or a related quantitative field.
  • Strong programming skills in Python, including experience with libraries such as Pandas, NumPy, and Scikit-learn.
  • Solid understanding of statistics, probability, hypothesis testing, and machine learning fundamentals.
  • Hands-on experience with data preparation, exploratory analysis, feature engineering, and model evaluation.
  • Experience developing and optimising predictive models for practical business applications.
  • Strong SQL skills and familiarity with relational databases and data extraction techniques.
  • Experience with data visualisation tools or libraries such as Matplotlib, Seaborn, Power BI, or Tableau is an advantage.
  • Familiarity with model deployment, APIs, cloud platforms, or machine learning pipelines is desirable.
  • Strong analytical thinking, problem-solving skills, and attention to detail.
  • Ability to explain technical concepts and communicate insights clearly to diverse stakeholders.
  • A proactive mindset, intellectual curiosity, and the ability to manage multiple analytical tasks independently.
  • A degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative discipline is preferred.
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