Position: Data Scientist
Department: Data Science
Location: Bangalore
Job Type: Full-time
Experience: 2–3 Years
Job Purpose
The Data Scientist will be responsible for analyzing complex datasets, building predictive models, and developing data-driven solutions to support business decision-making.
The ideal candidate should have strong analytical and problem-solving skills, hands-on experience in Python, SQL, statistics, and machine learning, and the ability to translate business problems into scalable analytical solutions.
Key Responsibilities
- Collect, clean, preprocess, and analyze structured and unstructured datasets.
- Perform exploratory data analysis to identify patterns, trends, relationships, and business opportunities.
- Build, train, validate, and optimize machine learning models.
- Develop predictive models using techniques such as regression, classification, clustering, and forecasting.
- Perform feature engineering and model selection based on business requirements.
- Write and optimize SQL queries for extracting and analyzing data.
- Work extensively with Python and relevant data science libraries.
- Evaluate model performance using appropriate statistical and machine learning metrics.
- Support the deployment and monitoring of machine learning models.
- Identify opportunities to automate manual analytical and data-processing activities.
- Develop data visualizations, dashboards, and presentations to communicate findings effectively.
- Collaborate with business, technology, product, and analytics teams to understand requirements and develop analytical solutions.
- Conduct statistical analysis and hypothesis testing as required.
- Maintain documentation related to models, datasets, methodologies, assumptions, and outcomes.
- Monitor existing models and support retraining or improvement wherever required.
- Present analytical findings and recommendations to business stakeholders in a simple and actionable manner.
- Stay updated with developments in Data Science, Machine Learning, Artificial Intelligence, and related technologies.
Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, Engineering, Economics, or a related quantitative field.
- Minimum 2–3 years of relevant experience in Data Science, Machine Learning, Advanced Analytics, or a similar role.
Knowledge, Skills & Abilities (KSAs)
Knowledge – What the person should know
- Strong understanding of statistical analysis and probability.
- Knowledge of supervised and unsupervised machine learning techniques.
- Understanding of regression, classification, clustering, and forecasting methods.
- Knowledge of feature engineering, model validation, and model evaluation techniques.
- Understanding of relational databases and SQL.
- Familiarity with data visualization concepts and analytical reporting.
- Basic understanding of machine learning model deployment and monitoring.
- Knowledge of Generative AI, NLP, LLMs, or deep learning will be an added advantage.
Skills – What the person should be able to do
- Strong proficiency in Python.
- Strong working knowledge of SQL.
- Hands-on experience with Python libraries such as:
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Ability to develop and evaluate machine learning models.
- Strong data cleaning and preprocessing skills.
- Experience handling large and complex datasets.
- Ability to create meaningful visualizations and dashboards.
- Exposure to Power BI or Tableau will be an advantage.
- Good analytical and problem-solving skills.
- Strong written and verbal communication skills.
- Ability to present complex data findings in a clear and understandable manner.
Abilities – Capacity to perform based on attributes
- Ability to understand business problems and translate them into analytical solutions.
- Ability to work independently on data science projects.
- Ability to collaborate effectively with technical and non-technical stakeholders.
- Ability to manage multiple projects and priorities simultaneously.
- Ability to identify data quality issues and recommend corrective actions.
- Ability to continuously learn new tools, techniques, and technologies.
- Strong attention to detail and accuracy.
Competency Matrix
| Competency | Level | Definition | Applicable |
| Python | Advanced | Ability to perform data analysis, modelling, and automation using Python | Yes |
| SQL | Advanced | Ability to extract, manipulate, and analyze data from relational databases | Yes |
| Machine Learning | Intermediate–Advanced | Ability to build, evaluate, and optimize ML models | Yes |
| Statistical Analysis | Advanced | Applies statistical methods to derive meaningful conclusions | Yes |
| Data Visualization | Intermediate | Creates clear visualizations and dashboards to communicate insights | Yes |
| Problem Solving | Advanced | Uses structured analytical thinking to solve complex business problems | Yes |
| Communication Skills | Advanced | Effectively presents technical findings to different stakeholders | Yes |
| Team Collaboration | Intermediate | Works effectively with analytics, technology, and business teams | Yes |
Preferred Skills
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Exposure to NLP, Generative AI, Large Language Models, or Deep Learning.
- Experience with time-series forecasting.
- Knowledge of Git or other version-control tools.
- Exposure to model deployment, APIs, and MLOps.
- Experience with Spark or big-data technologies will be an added advantage.
Work Environment
- Full-time role.
- Hybrid / Office / Remote as applicable.
- Requires regular coordination with business, product, analytics, and technology teams.
- May require working on multiple data science projects simultaneously.