Summary Of The Position
We are seeking a results-driven and solution-oriented
Data Scientist
to join our analytics team and contribute to key digital transformation initiatives. The ideal candidate possesses a strong foundation in
data analysis
, a solid grasp of
data engineering workflows
, and the confidence to collaborate closely with cross-functional stakeholders.This role offers an opportunity to work on meaningful datasets, deliver business insights, and design scalable data solutions that solve real-world problems. You should be comfortable working in both collaborative and independent environments, using modern tools and best practices in data science and analytics. You should be able to come up with business propositions that can be presented to business from analysing the various data sources, that can be used by business.In addition to core data science capabilities, the candidate should have awareness and hands-on exposure to Large Language Models (LLMs), an understanding of their selection and application to specific use cases, and commercial awareness of emerging AI technologies.
Key Accountabilities
- Data Analysis & Modelling:
- Explore, clean, and analyze structured and unstructured data to uncover insights and trends
- Develop statistical models and machine learning algorithms to support forecasting, segmentation, or classification use cases
- Select and fine-tune appropriate LLM models based on the use case, data availability, and performance requirements.
- Validate model performance and present recommendations with clear, data-backed narratives
- Support root cause analysis and define KPIs to measure business performance
- LLM Awareness & AI Solutioning:
- Demonstrate awareness of different LLM families (e.g., LLaMA, Bedrock, OpenAI models, Anthropic Claude, etc.) and their strengths/limitations.
- Evaluate and recommend the right AI model/toolkit for commercial viability, scalability, and compliance.
- Keep track of emerging AI/ML toolkits and frameworks, with an entrepreneurial mindset for identifying potential business applications.
- Solutioning & Collaboration:
- Work with business stakeholders to gather requirements and identify opportunities for data-driven solutions
- Translate business needs into data questions, develop hypotheses, and test solutions using analytical methods
- Collaborate with data engineers to ensure reliable and performant data pipelines
- Present findings and technical concepts to both technical and non-technical stakeholders
- Commercial awareness of LLM viability and selection of the models.
Data Engineering Awareness:
- Perform data wrangling and transformation using SQL, Python, or PySpark
- Understand data flow from source systems to analytics layers and assist in optimizing queries and transformations
- Contribute to designing efficient data workflows and suggest improvements for data quality and governance
Skills and Experience | Essential
- Core Skills:
- 4-7 years of hands-on experience in data science, analytics, or applied statistics
- Proficiency in Python (Pandas, NumPy, Scikit-learn) or R
- Strong experience writing optimized SQL queries for large datasets
- Ability to build and evaluate predictive models (e.g., regression, classification, clustering, time series)
- Ability to create use cases/business scenarios/proposals post analysing the data available.
- Experience in selecting and applying LLM models for business use cases.
- AI/LLM Expertise:
- Knowledge of LLM model families like LLaMA, Bedrock, OpenAI, Anthropic Claude, etc.
- Familiarity with AI toolkits, frameworks, and integration methods.
- Commercial awareness of LLM adoption, licensing considerations, and cost implications.
- Data Engineering Exposure:
- Familiarity with tools like Apache Airflow, Azure Data Factory, or Databricks
- Exposure to cloud platforms (Azure, AWS, or GCP) and big data concepts is an advantage
- Understanding of data pipeline components, ETL/ELT, and data lake/data warehouse concepts
- Visualization & Communication:
- Experience using Power BI, Tableau, or Python-based visualization libraries
- Strong data storytelling and visualization skills to communicate analytical results effectively
- Excellent written and verbal communication skills; ability to work closely with stakeholders from varied domains
- Compliance and Regulations:
- Candidates should demonstrate familiarity with data privacy and regulatory frameworks such as GDPR or HIPAA
Personal attributes
- Strong communication skills
- Analytical thinker with a proactive, solution-focused mindset
- Strong attention to detail and structured problem-solving ability
- Demonstrated ability to work on ambiguous problems and translate them into clear data questions
- Keen to learn and experiment with emerging data technologies and techniques
- Willingness to take ownership and initiative, suggest improvements, and drive quality
- Comfortable working in a dynamic environment across multiple clients/teams
- Commercial awareness and entrepreneurial spirits.
