{"id":750357,"url":"https://alion.io/job/nference-senior-data-scientist","title":"Senior Data Scientist","company":{"id":34471,"name":"Nference","domain":"nference.com","url":"https://alion.io/company/nference","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":51},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"BERT","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Interpretability","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NER","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"Kubernetes","optional":true},{"name":"LoRA","optional":true},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-09-10T07:49:38Z","employer_posted_date":"2026-09-10","last_verified_at":"2026-10-01T07:35:27Z","board_verified":true,"closed_at":null,"days_open":21,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":21},"description":"Senior Data Scientist\nLocation: Bangalore, India\nAbout nference\nnference 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.\nFounded 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.\nOur People\nAt nference, you'll work alongside software engineers, AI researchers, physicians, scientists, and product leaders who are passionate about solving meaningful, real-world problems.\nWe 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.\nThe Opportunity\nWe'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.\nYou 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.\nThe 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.\nAs 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.\nWhat You'll Do\nOwn complex machine learning problems from problem definition and data analysis through modelling, experimentation, evaluation, and productionization.\nBuild, train, fine-tune, and optimize machine learning and deep-learning models for real-world applications.\nWork with large-scale structuredandunstructured data, particularly clinical documents and EHR tables, while applying appropriate techniques to other data modalities when required.\nDevelop data strategies covering preprocessing, sampling, labeling, augmentation, dataset construction, and quality assessment.\nSelect and adapt appropriate model architectures based on the problem, data characteristics, and desired outcomes.\nDesign and conduct experiments by establishing baselines, comparing approaches, testing modelling or training strategies, and analyzing results.\nPerform error analysis to understand model behaviour, failure modes, and opportunities for improvement\nDevelop automated evaluation systems with appropriate methodologies to continuously evaluate and improve the systems in production.\nImprove models through systematic changes to data, architecture, training strategy, hyperparameters, and inference methods.\nDevelop NLP and transformer-based solutions for use cases such as classification, information extraction, NER, representation learning, retrieval, and ranking.\nApply LLM techniques such as embeddings, RAG, retrieval, prompting, and fine-tuning where they are appropriate to the underlying problem.\nMake informed trade-offs across model quality, generalization, robustness, latency, scalability, interpretability, and computational cost.\nCollaborate with Engineering and MLOps teams to deploy and maintain reliable ML models and pipelines in production.\nInvestigate production or near-production model performance and drive improvements based on observed results.\nContribute to scalable ML workflows covering data processing, training, evaluation, integration to apps, dockerization, deployment, and monitoring.\nMentor junior Data Scientists on modelling, experimentation, evaluation, debugging, and code quality.\nReview technical work and promote rigorous experimentation, reproducibility, and sound engineering practices.\nCollaborate with Product, Engineering, Clinical, and domain experts to translate business and scientific problems into effective ML solutions.\nEvaluate emerging research and techniques through experimentation and assess their applicability to nference's problems.\nContribute to technical standards and best practices for modelling, experimentation, and evaluation.\nDocument technical decisions, experimental findings, evaluation results, and key trade-offs.\nWhat We're Looking For\nRequired Qualifications\nBachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related technical field.\n4-7 years of professional experience in Data Science, Machine Learning, Applied AI, or a related field.\nStrong proficiency in Python and hands-on experience with PyTorch, TensorFlow, or similar deep-learning frameworks.\nStrong fundamentals in machine learning and deep learning, including model training, optimization, regularization, model selection, evaluation, and generalization.\nDemonstrated experience building, training, fine-tuning, and improving ML or deep-learning models.\nHands-on experience with unstructured data, particularly text or NLP data.\nStrong understanding of NLP concepts including text representation, embeddings, sequence modelling, attention mechanisms, and transformers.\nExperience working with deep-learning architectures such as BERT, transformers, or equivalent models.\nStrong experimentation skills, including baseline development, hypothesis testing, metric selection, comparative analysis, and iterative improvement.\nExperience performing error analysis and identifying model failure modes.\nStrong understanding of model evaluation and the ability to select metrics appropriate to the underlying ML problem.\nDemonstrated ability to improve model performance through changes to data, architecture, training, or inference approaches.\nAbility to evaluate trade-offs between model performance, robustness, generalization, latency, scalability, and cost.\nExperience taking ML solutions through data preparation, modelling, evaluation, and deployment.\nExperience with production or near-production ML systems and collaboration with engineering teams.\nDemonstrated ownership of significant ML projects or technical components.\nExperience mentoring junior Data Scientists or providing technical guidance.\nStrong analytical, problem-solving, written, and verbal communication skills.\nPreferred Qualifications\nExperience working with clinical, biomedical, healthcare, scientific, or other domain-specific datasets.\nExperience building NLP systems for large-scale text or document collections.\nExperience with LLMs, including embeddings, RAG, retrieval, fine-tuning, or evaluation.\nExperience with parameter-efficient fine-tuning techniques such as LoRA.\nExperience with information retrieval, ranking, vector search, or retrieval evaluation.\nExperience with large-scale model training or inference optimization.\nExperience with ML experiment tracking, model versioning, workflow orchestration, or MLOps platforms.\nExperience with Docker, Kubernetes, and cloud platforms such as AWS, GCP, or Azure.\nExperience with model serving, monitoring, scaling, and production performance optimization.\nExperience leading technical initiatives or working across Data Science, Engineering, Product, and domain teams.\nWhy Join nference?\nAt nference, you'll work on challenging ML problems involving large-scale clinical, molecular, imaging, and unstructured healthcare data.\nYou'll apply strong Data Science and deep-learning fundamentals to real-world problems where data quality, model performance, experimentation, and domain context matter.\nAs 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.\nBenefits & Perks\nIndustry Prestige: Build your career at an AI-first healthcare technology company working at the intersection of AI, software engineering, and biomedical research.\nCutting-Edge Innovation: Work on complex machine learning and AI problems involving clinical, molecular, imaging, and unstructured datasets.\nMeaningful Impact: Contribute to technologies that accelerate biomedical research and improve healthcare outcomes.\nGrowth & 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.\nWellness & 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.\nEqual Opportunity Employer\nnference 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.\nWe are proud to be an equal opportunity employer and welcome applicants from all backgrounds to join us in building technology that transforms healthcare.","description_format":"text","description_chars":9734,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning","Gym membership","Health insurance","Hybrid work","Professional development"],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Medical AI","Health Data & Interoperability"],"lifecycle":[{"event":"open","at":"2026-09-11T15:33:19Z"}],"liveness":{"score":73,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.814,"p_room":0.9,"age_days":20,"expected_fill_days":42,"reasons":["conf:17","velocity","win:mid"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/nference-senior-data-scientist","json_url":"https://alion.io/job/nference-senior-data-scientist.json","meta":{"generated_at":"2026-10-01T11:17:30Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2855,"day_limit":5000,"remaining_today":2145,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}