Applied Data Scientist - AI/ML

Applied Data Scientist

Full-Time | 3 to 6 Years Experience | Hybrid

Role Overview :

We are looking for a passionate and highly skilled Applied Data Scientist with a strong foundation in machine learning, statistical modelling, and real-world problem solving. The ideal candidate will have 36 years of hands-on experience in building, fine-tuning, and deploying end-to-end data science solutions, including experience with Large Language Models (LLMs) and Generative AI techniques. You will be responsible for translating complex business challenges into scalable, production-grade AI/ML solutions that power our next-generation products and services.

Key Responsibilities : Model Development & Research :

• Design and develop machine learning and deep learning models for NLP tasks including text classification, summarisation, sentiment analysis, and question answering.

• Fine-tune and adapt pre-trained LLMs (e.g., GPT, BERT, T5) to domain-specific use cases with a focus on performance and reliability.

Data Handling & Feature Engineering :

• Work with large-scale structured and unstructured datasets to build robust data pipelines, conduct exploratory data analysis, and engineer high-quality features.

• Ensure rigorous data preprocessing, augmentation, and validation to maximize model accuracy and generalization.

Algorithm Design & Optimisation :

• Collaborate with cross-functional teams to design and implement scalable algorithms optimised for production environments.

• Benchmark and continuously improve model performance using appropriate evaluation metrics and experimentation frameworks.

MLOps & Production Deployment :

• Own the end-to-end model lifecycle from experimentation and versioning to deployment, monitoring, and retraining.

• Build and maintain CI/CD pipelines for ML models using MLOps best practices and tooling (e.g., MLflow, Kubeflow, Airflow, SageMaker Pipelines).

• Deploy and scale machine learning models on cloud infrastructure (AWS, GCP, Azure), optimising for latency, throughput, and cost efficiency.

• Set up model monitoring, data drift detection, and automated alerting to ensure reliability in production.

Cross-Team Collaboration :

• Partner closely with product, engineering, and business teams to align AI/ML solutions with organisational goals.

• Translate ambiguous business problems into well-scoped data science problem statements with clear success criteria.

Research & Innovation :

• Stay current with state-of-the-art advancements in machine learning, NLP, and MLOps. Evaluate and adopt relevant new techniques into the team's workflow.