Senior Data Scientist
- Posted 2 months ago
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Job Description
We are seeking a Senior Data Scientist / Gen AI Developer who is passionate about transforming raw data into intelligent business insights and building AI models that solve real-world problems. In this role, you'll work across the entire data science lifecycle from exploratory analysis and statistical modelling to deploying advanced LLM-based and Generative AI solutions. You will be at the forefront of building AI-powered products across domains like healthcare, life sciences, finance, and enterprise analytics, with a focus on real-world impact and scalable architecture.
Responsibilities
Responsibilities
- Develop and deploy applications using Large Language Models (LLMs) like GPT-4 Claude, LLaMA, and Gemini.
- Build GenAI-powered tools such as AI report generators, contextual chatbots, summarisation engines, and semantic search applications.
- Design, optimise, and evaluate prompts tailored for various use cases and user personas.
- Implement and enhance Retrieval-Augmented Generation (RAG) workflows for improved relevance and accuracy.
- Fine-tune and customise foundation models using techniques like LoRA, PEFT, and prompt-tuning.
- Evaluate and monitor model performance on accuracy, latency, and business-specific KPIs.
- Apply NLP techniques such as entity recognition, topic modelling, summarisation, sentiment analysis, and question answering.
- Work with libraries and frameworks such as Hugging Face Transformers, LangChain, spaCy, and NLTK.
- Deploy GenAI applications using cloud platforms like AWS Bedrock/SageMaker, GCP Vertex AI, or Azure OpenAI.
- Build scalable and secure inference pipelines integrated with backend systems and APIs.
- Use tools like MLflow and Docker for model lifecycle management and MLOps best practices.
- Collaborate with cross-functional teams including product managers, data engineers, and designers to align AI outputs with business needs.
- Continuously experiment with new GenAI models, tools, and research to keep the solutions cutting-edge.
- 7+ years of experience as a Data Scientist with good experience in Generative AI or LLM-based applications.
- Strong expertise in Python and libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch.
- Experience in end-to-end ML lifecycle including model development, validation, and deployment.
- Solid understanding of statistics, probability, and data modelling.
- Experience with NLP libraries (NLTK, spaCy, Hugging Face Transformers).
- Hands-on experience with GenAI tools: OpenAI APIs, LangChain, RAG, LLM fine-tuning.
- Experience with SQL and at least one NoSQL database.
- Familiarity with cloud platforms (AWS, GCP, Azure) and version control (Git).
- Strong communication skills with the ability to explain complex models to non-technical audiences.
More Info
Key Skills
About Company
Skills:
snowflake , Tensorflow, Pytorch, Python, Statistical Analysis, Unsupervised Learning, Sql, Pandas, Spark, MLflow, Agentic AI systems, MLOps practices, Semantic retrieval systems, Kubeflow, Generative AI LLM Proficiency, Optimization Techniques, RAG pipelines, Vector databases, Scikit-learn, Correlation Analysis, MCP Model Context Protocol, supervised learning, Time-series analysis, Prompt Engineering, Advanced AI ML Development
Skills:
Natural language processing (NLP), Java, Statistical Analysis, Tensorflow, Pytorch, Gcp, Opencv, Azure, Python, AWS, scikit-learn, Data preprocessing, GPT, CNNs, Feature engineering, R, YOLO, BERT
Skills:
Tensorflow, Python, Java, Hibernate, Hadoop, Big Data, Machine Learning Algorithms, Spring, DataBricks, Ms Azure, Jenkins, Git, Hive, Azure Data Factory, Pytorch, Spark, Keras, Langchain, MLLib, HDInsight, Deep Neural Networks, Map-Reduce, Gen AI infrastructure, HDFS, RAG
Skills:
Pyspark, Algorithms, Nlp, Azure Functions, Docker, Databricks, Azure Machine Learning, Python, GenAI, AKS, Azure Services, Applied Statistics, Azure AI Search, Retrieval Augmented Generation, Large Language Models, Azure Kubernetes Service
Skills:
Azure Data Factory, Azure Databricks, Statistical modeling for access risk analysis, SQL and NoSQL database management, Implementation of security and compliance controls in Azure, Microsoft Azure Active Directory administration, Microsoft Azure Privileged Identity Management PIM, Power BI or similar data visualization tools, Python or R for data analysis and automation, REST APIs and integration with external data sources, Cloud governance and policy management, Azure Sentinel or other SIEM platforms, Certification in advanced Azure security solutions, Automation tools such as Terraform or Azure ARM templates, Machine learning algorithms for security threat detection