Senior Data Scientist

We are seeking a Senior Data Scientist with 5–6 years of experience in machine learning and deep learning to join our dynamic team. The ideal candidate will have a proven track record in designing and implementing advanced ML/DL algorithms, fine-tuning large language models (LLMs), and a good understanding of MLOps/LLMOps practices. Strong communication skills and the ability to engage in presales activities and client-facing discussions are highly desirable.

Key Responsibilities:

ML/DL Algorithm Development

• Design, build, and optimize machine learning and deep learning models for various business use cases.
• Conduct exploratory data analysis, feature engineering, and model selection to ensure high-performance solutions

LLM Fine-tuning

• Work with state-of-the-art large language models to adapt them to specific tasks or domains.
• Identify and implement best practices for prompt engineering, tokenization, and hyperparameter tuning.

MLOps & LLMOps (Good to Have)

• Collaborate with engineering teams to develop and deploy ML pipelines using MLOps or emerging LLMOps frameworks.
• Automate data ingestion, model training, and model serving for reproducibility and scalability.

Presales & Client Engagement (Good to Have)

• Support sales teams by contributing technical expertise during presales activities and solution demonstrations.
• Understand client requirements, provide consultative solutions, and articulate technical concepts to non-technical stakeholders.

Collaboration & Communication

• Work closely with cross-functional teams including product managers, data engineers, and other stakeholders.
• Present findings, insights, and project updates to both technical and non-technical audiences clearly and concisely.

Required Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.

Technical Skills:

• Proficiency in Python (preferred) or R, with experience using libraries/frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of classical machine learning algorithms (e.g., SVM, Random Forest, Gradient Boosting) and deep learning architectures (CNNs, RNNs, Transformers).
• Hands-on experience fine-tuning or customizing large language models (e.g., GPT, BERT, T5).

Soft Skills:

• Excellent verbal and written communication skills.
• Demonstrated ability to work collaboratively in a team environment.
• Strong problem-solving and analytical thinking capabilities.