Data Scientist II

Role overview

Data Scientist II at Nference

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 and skills

• 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?
• At nference, you'll work on challenging ML problems involving large-scale clinical, molecular, imaging, and unstructured healthcare data.
• You'll apply strong Data Science and deep-learning fundamentals to real-world problems where data quality, model performance, experimentation, and domain context matter.
• As a Senior Data Scientist, you'll remain deeply hands-on while taking ownership of complex technical problems, mentoring other Data Scientists, and contributing to the team's modelling and experimentation practices.
• 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 nference is committed to building a diverse, equitable, and inclusive workplace where everyone has the opportunity to thrive.
• We celebrate different perspectives, backgrounds, and experiences because they strengthen our teams and drive innovation.
• We are proud to be an equal opportunity employer and welcome applicants from all backgrounds to join us in building technology that transforms healthcare.

Employment details

• Location: Bangalore, India
• Location: Bengaluru [KA] [India]

About the employer

• nference is an AI-first healthcare technology company dedicated to accelerating biomedical discovery and transforming healthcare through artificial intelligence and large-scale computing.
• Our platforms help pharmaceutical companies, healthcare providers, and research organizations unlock insights from complex clinical, molecular, and imaging data to drive better decisions and improve patient outcomes.
• Founded with the vision of solving some of healthcare's most challenging problems, nference combines expertise in software engineering, machine learning, data science, and life sciences to build products that make a meaningful impact on global healthcare.
• Our People At nference, you'll work alongside software engineers, AI researchers, physicians, scientists, and product leaders who are passionate about solving meaningful, real-world problems.
• We foster a culture of ownership, curiosity, collaboration, technical excellence, and continuous learning.
• Our teams are encouraged to challenge assumptions, contribute ideas, and take ownership of their work from problem definition and experimentation through production.
• The Opportunity We're looking for a Senior Data Scientist with 4–7 years of experience and strong hands-on expertise in Data Science, machine learning, and deep learning , particularly with unstructured and text data .
• You will work on complex ML problems across the full lifecycle, from problem formulation and data strategy to model development, experimentation, evaluation, and productionization.
• The role requires strong technical depth and the ability to make sound modelling decisions based on data, experiments, and measurable outcomes.
• The ideal candidate has experience building, training, fine-tuning, evaluating, and improving models , rather than primarily using pre-trained models or AI APIs.
• Experience with NLP, transformers, and LLMs is valuable, but strong fundamentals in machine learning and deep learning are essential.
• As a senior individual contributor, you will own significant technical problems, mentor junior Data Scientists, contribute to technical direction, and collaborate with Engineering, Product, Clinical, and domain teams.
• 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

Additional role details

• Senior Data Scientist