Data Scientist

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

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, Engineering, or related quantitative disciplines.
· Minimum 2+ years of experience in Data Science, Machine Learning, or Analytics roles.
· Experience in Healthcare, Wellness, HR Tech, SaaS, or Product-based organizations preferred.
· Strong hands-on experience in Python, SQL, and Machine Learning algorithms.
· Experience working with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or similar technologies.
· Exposure to cloud platforms such as AWS, Azure, or GCP preferred.
· Experience with NLP, Deep Learning, MLOps, or Generative AI will be an added advantage.

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:
o Pandas
o NumPy
o Scikit-learn
o 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
• 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.

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.