Senior Data Scientist

Location: Bengaluru, India

Department: R&D-Technology

Experience: 4 - 7 years

Senior Data Scientist

About nference

Our People

The Opportunity

What You'll Do

• Own complex machine learning problems from problem definition and data analysis through modelling, experimentation, evaluation, and productionization.
• Build, train, fine-tune, and optimize machine learning and deep-learning models for real-world applications.
• Work with large-scale structured and unstructured data, particularly clinical documents and EHR tables, while applying appropriate techniques to other data modalities when required.
• Develop data strategies covering preprocessing, sampling, labeling, augmentation, dataset construction, and quality assessment.
• Select and adapt appropriate model architectures based on the problem, data characteristics, and desired outcomes.
• Design and conduct experiments by establishing baselines, comparing approaches, testing modelling or training strategies, and analyzing results.
• Perform error analysis to understand model behaviour, failure modes, and opportunities for improvement
• Develop automated evaluation systems with appropriate methodologies to continuously evaluate and improve the systems in production.
• Improve models through systematic changes to data, architecture, training strategy, hyperparameters, and inference methods.
• Develop NLP and transformer-based solutions for use cases such as classification, information extraction, NER, representation learning, retrieval, and ranking.
• Apply LLM techniques such as embeddings, RAG, retrieval, prompting, and fine-tuning where they are appropriate to the underlying problem.
• Make informed trade-offs across model quality, generalization, robustness, latency, scalability, interpretability, and computational cost.
• Collaborate with Engineering and MLOps teams to deploy and maintain reliable ML models and pipelines in production.
• Investigate production or near-production model performance and drive improvements based on observed results.
• Contribute to scalable ML workflows covering data processing, training, evaluation, integration to apps, dockerization, deployment, and monitoring.
• Mentor junior Data Scientists on modelling, experimentation, evaluation, debugging, and code quality.
• Review technical work and promote rigorous experimentation, reproducibility, and sound engineering practices.
• Collaborate with Product, Engineering, Clinical, and domain experts to translate business and scientific problems into effective ML solutions.
• Evaluate emerging research and techniques through experimentation and assess their applicability to nference's problems.
• Contribute to technical standards and best practices for modelling, experimentation, and evaluation.
• Document technical decisions, experimental findings, evaluation results, and key trade-offs.

What We're Looking For

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related technical field.
• 4–7 years of professional experience in Data Science, Machine Learning, Applied AI, or a related field.
• Strong proficiency in Python and hands-on experience with PyTorch, TensorFlow, or similar deep-learning frameworks.
• Strong fundamentals in machine learning and deep learning, including model training, optimization, regularization, model selection, evaluation, and generalization.
• Demonstrated experience building, training, fine-tuning, and improving ML or deep-learning models.
• Hands-on experience with unstructured data, particularly text or NLP data.
• Strong understanding of NLP concepts including text representation, embeddings, sequence modelling, attention mechanisms, and transformers.
• Experience working with deep-learning architectures such as BERT, transformers, or equivalent models.
• Strong experimentation skills, including baseline development, hypothesis testing, metric selection, comparative analysis, and iterative improvement.
• Experience performing error analysis and identifying model failure modes.
• Strong understanding of model evaluation and the ability to select metrics appropriate to the underlying ML problem.
• Demonstrated ability to improve model performance through changes to data, architecture, training, or inference approaches.
• Ability to evaluate trade-offs between model performance, robustness, generalization, latency, scalability, and cost.
• Experience taking ML solutions through data preparation, modelling, evaluation, and deployment.
• Experience with production or near-production ML systems and collaboration with engineering teams.
• Demonstrated ownership of significant ML projects or technical components.
• Experience mentoring junior Data Scientists or providing technical guidance.
• Strong analytical, problem-solving, written, and verbal communication skills.

Preferred Qualifications

• Experience working with clinical, biomedical, healthcare, scientific, or other domain-specific datasets.
• Experience building NLP systems for large-scale text or document collections.
• Experience with LLMs, including embeddings, RAG, retrieval, fine-tuning, or evaluation.
• Experience with parameter-efficient fine-tuning techniques such as LoRA.
• Experience with information retrieval, ranking, vector search, or retrieval evaluation.
• Experience with large-scale model training or inference optimization.
• Experience with ML experiment tracking, model versioning, workflow orchestration, or MLOps platforms.
• Experience with Docker, Kubernetes, and cloud platforms such as AWS, GCP, or Azure.
• Experience with model serving, monitoring, scaling, and production performance optimization.
• Experience leading technical initiatives or working across Data Science, Engineering, Product, and domain teams.

Why Join nference?

Benefits & Perks

• Industry Prestige: Build your career at an AI-first healthcare technology company working at the intersection of AI, software engineering, and biomedical research.
• Cutting-Edge Innovation: Work on complex machine learning and AI problems involving clinical, molecular, imaging, and unstructured datasets.
• Meaningful Impact: Contribute to technologies that accelerate biomedical research and improve healthcare outcomes.
• Growth & Flexibility: Work in a collaborative, innovation-driven environment with continuous learning opportunities and a hybrid work model for eligible employees after successful completion of the three-month probation period.
• Wellness & Perks: Enjoy reimbursements for gym memberships, technology gadgets, high-speed internet, professional development, comprehensive health insurance, and complimentary breakfast, lunch, and snacks at our Bangalore office.

Equal Opportunity Employer