Data Scientist

Job title: Data Scientist

Duration: 6 months

Location: Remote

We are seeking a skilled Data Scientist with a strong focus on developing models leveraging conventional AI and GenAI techniques. The ideal candidate will have experience in designing and executing data science experiments, developing scalable and robust machine learning models, implementing data pipelines and deploying AI solutions in production.

Responsibilities

• Building of GenAI and AI solutions, including but not limited to analytical model development and implementation, prompt engineering, general all-purpose programming (e.g., Python), testing, communication of results, front-end and back-end integration, and iterative development with clients.

• Collaborating with business teams to understand their business problem, gather requirements, create an initial hypothesis, and develop and deploy of GenAI and AI solution approach.

• Designing and solving AI/GenAI architectures for business teams, specifically for plugin-based solutions and custom AI/GenAI application builds.

• Leading and contributing to the development of proof of concepts, pilots, and production use cases for business teams while working with cross-functional teams.

• Generate useful insights based on iterative data analysis and make appropriate recommendations to business partners.

• Continuously monitor and improve the performance of AI solutions through data analysis and testing.

• Strong communication to help business partners better understand the use of data, ML models and AI solutions.

Qualification Required

Master’s degree in a quantitative field such as Statistics, Applied Mathematics, Data Science, Engineering, or Computer Science or Physics.

• Proficient in programming using Python and SQL.

• Experience in using Python (e.g., Pandas, NLTK, Scikit-learn, Keras, etc.), common LLM development frameworks (e.g., Langchain, Semantic Kernel), Relational storage (SQL), Non-relational storage (NoSQL).

• At least 2 years of industry experience in developing and deploying models using conventional AI and GenAI techniques.

• Hands-on experiences in developing NLP models (Document Classification, Entity Extraction, Entity Relation Extraction, etc.).

• Proven problem-solving abilities, including conducting root cause analysis to address specific business inquiries and identify opportunities for enhancement.

• Demonstrated expertise in the data analytics life cycle, encompassing problem framing, data collection, data cleansing, insights generation, reporting, and communication.

• Skilled in the machine learning modelling life cycle, including exploratory data analysis, data cleansing, feature engineering, model building, deployment and monitoring.

• Good understanding of vectorisation and embedding, prompt engineering, RAG, and multi-agent techniques.

• Solid knowledge and highly skilled in supervised and unsupervised machine learning algorithms, deep learning, and LLMs.

• Experience in developing and deploying models in cloud-based environments, specifically Microsoft Azure, and Databricks, following MLOps best practices.

• Experience with Git Version Control, Unit/Integration/End-to-End Testing, CI/CD, release management, etc