6446 Data Scientist jobs in india

Job DescriptionPrincipal Data Scientist / Sr Data Scientists /Data Scientist / Jr. Data Scientist / Data Science Intern / Machine Learning Engineer-Global Tech AI Health Startup.Joining : Immediately.Global HQ, Benovymed Global Technology Development and Innovation Center & Benovymed Global R and D, and AI Health Innovation Center in Delhi NCR.Job Role Responsibility Description :We are looking for passionate, Strong experience with an entrepreneur mindset to join us as Hardcore Full Stack Data Scientist end-to-end single-handed multi-hat role who is already working in applied AI in ML, deep Learning, ANN, and CNN platform specifically working as single-handed doers or in a small Data Science Team with full end to end ownership of Data Science work commitment delivery as Data Scientist working in any reputed AI Data Science is driven Tech startup preferably in Healthcare.Required Skillsets :- Needs to have statistical, mathematical, and predictive modeling as well as business strategy skills to build the algorithms necessary to ask the right questions and find the right answers.- Need to be able to communicate their findings, orally and visually.- Need to understand how the products are developed and even more important, as big data touches the privacy of consumers, they need to have a set of ethical responsibilities.-- Need to have a natural desire to go beneath the surface of a problem.- Confident and self-secure as they more often than not will have to deal with situations where there is a lot of unknown.- Good knowledge & experience in Artificial Neural Network ( ANN ), AI conversational Chatbot & Needs to be able to write the required Algorithm and coding programming.- Preferably in different programming languages such as Python, Tensor Flow, Keras, AWS (ML and DL), Numpy, and Pytorch.In addition the need to be familiar with disciplines as follows :- Natural Language Processing, Machine learning, Conceptual modeling, Statistical analysis, Predictive Modeling, and Hypothesis testing.Data scientists should have at least some of the following capabilities :- Cleaning data, formatting data, Having the ability to query databases & perform statistical analysis, building AI Data Modelling, validating the AI Model, and deployment.- Being able to develop or program databases.- Having a good understanding of design & architecture principles.Qualification :B.Tech / M.Tech in CSE / Electrical / Electronics & Communication/ Maths & Computing /Statistics /BioTech/BioStat / Clinical Engineering in Data Science / Machine Learning from Top-rated Tech University / Top rated IIT /IIIT.Mandatory Requirement :Experience :- Post last qualification of at least 0 to 7 Years of strong experience exclusive in real Industry Projects in applied AI ML & Deep Learning Data Science in an early-stage AI Health startup at the role and designation as a single-handed full-stack Principal Data Scientist / Sr. Data Scientist.- Hardcore in ANN /Deep Learning /Machine Learning/NLP with at least 2-20 good Applied AI-oriented Applied Research Projects & applications with hardcore experience working on ANN, CNN, RNN, and deep learning in Digital Health startups to solve specific complex problems.CTC :- Best Package in Industry.- Terms & Conditions Apply as per our organization.- To deserving eligible candidates only, the company Equity / ESOP may be given also as a part of a Package for a long-term career opportunity.- Apply with your CV with passport size photo, & current CTC, expected CTC, Notice Period, Project details & cover letter.KeywordsData Science,Data Scientist,Machine Learning,Python,Tensorflow,Numpy,Data Modeling,Artificial Intelligence,NLP,Deep Learning*

We are looking for a Data Scientist with expertise in Generative AI to design, build, and deploy intelligent solutions using advanced machine learning and deep learning models. The ideal candidate will have a strong background in data science, natural language processing (NLP), and hands-on experience with large language models (LLMs).

• Proven experience as a Data Scientist or ML Engineer.
• Strong knowledge of Python and libraries like PyTorch, TensorFlow, Transformers, scikit-learn, pandas, NumPy.
• Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude, Gemini, etc.) and prompt engineering.
• Experience with retrieval-augmented generation (RAG), LangChain, or vector databases (e.g., FAISS, Pinecone, Chroma).
• Solid understanding of data preprocessing, feature engineering, and model evaluation.
• Exposure to cloud platforms (AWS, GCP, Azure) and MLOps tools is a plus.
• Excellent problem-solving, analytical, and communication skills.

Good to Have:

• Experience in multimodal AI (text, image, audio).
• Familiarity with API integrations for LLMs.
• Contributions to open-source AI projects.
• Knowledge of ethical AI practices and responsible model deployment.

Designation - Data Scientist

Experience - 3+ Years

Job Overview:

We are seeking a passionate and skilled Data Scientist with 3 - 5 years of hands-on experience in AI/ML, LLMs, and deployment workflows. The ideal candidate will have experience in LangGraph, LangGroid, LangChain, and Langfuse, and a strong foundation in cloud platforms (Azure, GCP). You will work on building AI agents, RAG pipelines, chatbots, voice agents, and OCR solutions while fine-tuning and deploying models using modern frameworks and tools.

Key Responsibilities:

• Design and develop scalable AI solutions using LLM frameworks like LangChain, LangGraph, LangGroid, and Langfuse.
• Implement and optimize Retrieval-Augmented Generation (RAG) pipelines.
• Build and deploy intelligent chatbots and voice agents with natural language understanding.
• Develop data-driven applications using FastAPI, Flask, or Streamlit.
• Perform OCR and document intelligence tasks using AI/ML models.
• Fine-tune transformer models for custom use cases.
• Implement and manage automated data workflows with n8n or similar tools.
• Engineer high-quality prompts for LLM prompt engineering and prompt chaining.
• Design, train, evaluate, and deploy custom models on Azure and GCP.
• Collaborate with cross-functional teams on model deployment, scalability, and performance monitoring.

Data Scientist

Responsibilities

●Design and implement AI agent workflows. Develop end-to-end intelligent pipelines and multi-agent systems (e.g., LangGraph/LangChain workflows) that coordinate multiple LLM-powered agents to solve complex tasks. Create graph-based or state-machine architectures for AI agents, chaining prompts and tools as needed.

●Build and fine-tune generative models. Develop, train, and fine-tune advanced generative models (transformers, diffusion models, VAEs, GANs, etc.) on domain-specific data. Deploy and optimize foundation models (such as GPT, LLaMA, Mistral) in production, adapting them to our use cases through prompt engineering and supervised fine-tuning.

●Develop data pipelines. Build robust data collection, preprocessing, and synthetic data generation pipelines to feed training and inference workflows. Implement data cleansing, annotation, and augmentation processes to ensure high-quality inputs for model training and evaluation.

●Implement LLM-based agents and automation. Integrate generative AI agents (e.g., chatbots, AI copilots, content generators) into business processes to automate data processing and decision-making tasks. Use Retrieval-Augmented Generation (RAG) pipelines and external knowledge sources to enhance agent capabilities. Leverage multimodal inputs when applicable.

●Optimize performance and safety. Continuously evaluate and improve model/system performance. Use GenAI-specific benchmarks and metrics (e.g., BLEU, ROUGE, TruthfulQA) to assess results, and iterate to optimize accuracy, latency, and resource efficiency. Implement safeguards and monitoring to mitigate issues like bias, hallucination, or inappropriate outputs.

●Collaborate and document. Work closely with product managers, engineers, and other stakeholders to gather requirements and integrate AI solutions into production systems. Document data workflows, model architectures, and experimentation results. Maintain code and tooling (prompt libraries, model registries) to ensure reproducibility and knowledge sharing.

Required Skills & Qualifications

●Education: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative (or equivalent practical experience). A strong foundation in algorithms, statistics, and software engineering is expected.

●Programming proficiency: Expert-level skills in , with hands-on experience in machine learning and deep learning frameworks (PyTorch, Comfortable writing production-quality code and using version control, testing, and code review workflows.

●Generative model expertise: Demonstrated ability to build, fine-tune, and deploy large-scale generative Familiarity with transformer architectures and generative techniques (LLMs, diffusion models, Experience working with model repositories and fine-tuning frameworks (Hugging Face, etc.).

●LLM and agent frameworks: Strong understanding of LLM-based systems and agent-oriented AI patterns. Experience with frameworks like LangGraph/LangChain or similar multi-agent Knowledge of agent communication standards (e.g., MCP/Agent to enable interoperability between AI agents.

●AI integration and MLOps: Experience integrating AI components with existing systems via APIs and services. Proficiency in retrieval-augmented generation (RAG) setups, vector databases, and prompt Familiarity with machine learning deployment and MLOps tools (Docker, Kubernetes, MLflow, KServe, etc.) for managing end-to-end automation and scalable

●Familiarity with GenAI tools: Hands-on experience with state-of-the-art GenAI models and APIs (OpenAI GPT, Anthropic, Claude, etc.) and with popular libraries (Hugging Face Transformers, LangChain, etc.). Awareness of the current GenAI tooling ecosystem and best practices.

●Soft skills: Excellent problem-solving and analytical abilities. Strong communication and teamwork skills to collaborate across data, engineering, and business teams. Attention to detail and a quality-oriented mindset. (See Ideal Candidate below for more on personal attributes.)

Ideal Candidate

●Innovative, problem-solver: You are a creative thinker who enjoys tackling open-ended challenges. You have a solutions-oriented mindset and proactively experiment with new ideas and

●Systems thinker: You understand how different components (data, models, services) fit together in a large system. You can architect end-to-end AI solutions with attention to reliability, scalability, and integration points.

●Collaborative communicator: You work effectively in multidisciplinary teams. You are able to explain complex technical concepts to non-technical stakeholders and incorporate feedback. You value knowledge sharing and mentorship.

●Adaptable learner: The generative AI landscape evolves rapidly. You are passionate about staying current with the latest research and tools. You embrace continuous learning and are eager to upskill and try new libraries or

●Ethical and conscientious: You care about the real-world impact of AI systems. You take responsibility for the quality and fairness of models, and proactively address concerns like data privacy, bias, and security

Salary : 30 to 45 LPA

Exp: 7 to 12 years

Location :Mumbai Only

Notice: Immediate to 30 days.

Roles & responsibilities:

7+ years exp on Python, ML and Banking model development

• Interact with the client to understand their requirements and communicate / brainstorm solutions, model Development: Design, build, and implement credit risk model.
• Contribute to how analytical approach is structured for specification of analysis
• Contribute insights from conclusions of analysis that integrate with initial hypothesis and business objective. Independently address complex problems
• 3+ years exp on ML/Python (predictive modelling).
• Design, implement, test, deploy and maintain innovative data and machine learning solutions to accelerate our business.
• Create experiments and prototype implementations of new learning algorithms and prediction techniques
• Collaborate with product managers, and stockholders to design and implement software solutions for science problems
• Use machine learning best practices to ensure a high standard of quality for all of the team deliverables
• Has experience working on unstructured data ( text ): Text cleaning, TFIDF, text vectorization
• Hands-on experience with IFRS 9 models and regulations.
• Data Analysis: Analyze large datasets to identify trends and risk factors, ensuring data quality and integrity.
• Statistical Analysis: Utilize advanced statistical methods to build robust models, leveraging expertise in R programming.
• Collaboration: Work closely with data scientists, business analysts, and other stakeholders to align models with business needs.
• Continuous Improvement: Stay updated with the latest methodologies and tools in credit risk modeling and R programming.

Scope
You will work closely with customers, product teams, and engineering to:

• Onboard new clients and configure solutions to their data and business needs.
• Validate data quality and integrity.
• Deploy and monitor machine learning models in production.
• Execute existing ML pipelines to train new models and assess their quality.
• Interpret model performance and provide insights to both customers and internal teams.
• Communicate technical concepts clearly to non-technical stakeholders.
• Provide actionable feedback to product and R&D teams based on field experience.

Our Technical Environment

• Languages: Python 3.*, SQL
• Frameworks/Tools: TensorFlow, PyTorch, Pandas, NumPy, Jupyter, Flask
• Big Data & Cloud: Snowflake, Apache Beam/Spark, Azure, GCP
• DevOps & Monitoring: Docker, Kubernetes, Kafka, Pub/Sub, Jenkins, Git, TFX, Dataflow

What You'll Do

• Collaborate with customers and internal teams to understand data, business context, and deployment requirements.
• Perform data validation, enrichment, and transformation to ensure readiness for modelling.
• Execute pre-built ML pipelines to train and retrain models using customer data.
• Evaluate and interpret model performance metrics to ensure quality and stability.
• Monitor model behaviour and data drift in production environments.
• Troubleshoot issues related to data pipelines, model behaviour, and system integration.
• Clearly explain model behaviour, configuration, and performance to customer stakeholders.
• Gather insights from customer engagements and provide structured feedback to product and engineering teams to drive product enhancements.
• Document processes and contribute to playbooks for scalable onboarding.
• Train and mentor junior PS team members.

What We're Looking For

• Bachelor's or master's in computer science, Data Science, or related field with 5 to 10yrs of experience
• Strong understanding of machine learning and data science fundamentals.
• Proven experience deploying and supporting ML models in production.
• Experience executing ML pipelines and interpreting model performance.
• Excellent problem-solving and debugging skills.
• Strong communication skills with the ability to explain complex technical topics to non-technical audiences.
• Experience working directly with customers or cross-functional teams.
• Familiarity with monitoring tools and best practices for production ML systems.
• Experience with cloud platforms (Azure or GCP preferred).
• Bonus: Experience in supply chain, retail, or similar domains.

Our Values
If you want to know the heart of a company, take a look at their values. Ours unite us. They are what drive our success – and the success of our customers. Does your heart beat like ours? Find out here: Core Values

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.

About Us:

At Valiance, we empower businesses with AI-driven insights and data analytics solutions. Specializing in demand forecasting within retail Industry very specific to
Apparel and Footwear
domains, we leverage standardized ML models to deliver accurate forecasting outcomes to our clients. We are looking for a skilled Data Scientist who can interpret and improve these models to drive real-world business results.

Role Overview:

The Data Scientist will be responsible for applying our pre-trained demand forecasting models to generate actionable insights and highly accurate forecasting outcomes. This role involves thorough data analysis, identifying trends and anomalies, interpreting results, and communicating findings effectively to a non-technical business audience. The ideal candidate is less focused on creating algorithms and more oriented towards solving business challenges and providing insights that make a tangible impact.

Key Responsibilities:

• Data Analysis:
  Conduct in-depth data analysis to identify trends, patterns, and anomalies in demand/Timeseries forecasting for Retail Industry very specific to Apparel and Footwear (must have)
• Model Utilization:
  Leverage existing pre-trained forecasting models, optimizing their performance by incorporating a nuanced understanding of data points and improving model outcomes based on data insights.
• Interpretation & Communication:
  Interpret model results and explain outcomes in simple, actionable terms for business stakeholders, ensuring clarity and relevance.
• Insights Generation:
  Develop insights that guide business decisions, aiming for highly accurate forecasting outcomes to meet business requirements.
• Collaboration:
  Work closely with cross-functional teams, including business stakeholders and analysts, to ensure forecasting outputs align with business objectives and provide real-world value.
• Continuous Improvement:
  Identify opportunities to improve model performance through better data usage and fine-tuning, rather than new model development.

Required Skills & Qualifications:

• Experience:
  3-6 years of experience
  in data science or a related field, specifically in
  Demand/Timeseries forecasting for Retail Industry very specific to Apparel and Footwear (must have)
• Technical Proficiency:
  Strong skills in data analysis, data visualization, and working with pre-trained ML models. Proficiency in Python, and SQL is preferred.
• Business Focus:
  Strong orientation towards solving business problems rather than a pure focus on machine learning algorithms.
• Communication Skills:
  Ability to clearly communicate insights and forecast results to non-technical stakeholders in simple, understandable language.
• Problem Solving:
  Demonstrated ability to interpret data, uncover actionable insights, and suggest practical solutions for business needs.
• Detail-Oriented:
  Thorough attention to detail, ensuring accuracy in forecasting and relevance of insights.
• Educational Background:
  Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related field.

*Need Financial Domain Exp

Position: Data Scientist

Location: Noida

Work Mode: Onsite Setup

As an Applied/ Data scientist, you will contribute to the design, development, and improvement of AI/ML models and systems. You will be expected to:

• Work with applied scientists, data scientist, software engineers and product partners to design and deliver AI/ML solutions in production at scale.

• Design and develop AI/ML models, workflows and end-to-end pipelines for data preparation, training, deployment, monitoring, etc., and ensure the quality of architecture and design of our ML models and systems.

• Deliver customer-facing and/or underlying AI/ML solutions that empower our platform to provide the best experience for our customers.