530 Data Scientist jobs in Hyderabad

Job Description

• 6+ Years of expertise in the relevant field.
• Pre-process structured and unstructured data from various sources.
• Develop and deploy machine learning and AI models for classification, regression, clustering, and recommendation systems.
• Continuously monitor model performance and update models as needed.
• Translate complex business problems into data science solutions and communicate insights effectively to non-technical stakeholders.
• Statistical and Analytical Expertise: Strong foundation in statistical theory, hypothesis testing, and data summarization. Ability to select and apply appropriate modeling techniques.
• Model Development Lifecycle: Familiarity with the full ML lifecycle including feature engineering, model selection, validation, deployment, and monitoring.
• MBA graduate in statistics, data science, mathematics or a relative quantitative field

Tech Stack

• Python (NumPy, pandas, scikit-learn, matplotlib, seaborn)
• AWS sagemaker
• H20.ai
• SQL

Role Highlights:

Position: Data Scientist

Experience: 1+ years

Location: Hyderabad- Hybrid

Notice Period: Immediate/7 days joiners mandate

Job Overview:

Must have skills- Python, Generative AI, RAG, LLM

Good to have- Agentic AI and ML Ops

About CoffeeBeans Consulting

CoffeeBeans is a fast-growing tech company delivering high-quality software and AI solutions to clients across industries. We combine deep technical expertise with an agile mindset to solve complex business problems. Our data science and GenAI teams work at the frontier of ML, NLP, and product development to build intelligent, scalable, and user-centric solutions.

Role Overview

As an L1 Data Scientist, you will work alongside experienced engineers and data scientists to solve real-world problems using ML and GenAI. In addition to classical data science work, we expect you to actively contribute to building and fine-tuning large language model (LLM)–based applications, including chatbots, copilots, and automation workflows.

This role is ideal for early-career professionals with a strong analytical mindset, a passion for ML/AI, and curiosity around the fast-evolving world of generative AI.

Key Responsibilities

• Work with business stakeholders to understand problem statements and assist in translating them into data science tasks.
• Perform data collection, cleaning, feature engineering, and exploratory data analysis.
• Develop and evaluate machine learning models using Python and ML libraries (e.g., scikit-learn, XGBoost).
• Assist in building LLM-based workflows such as RAG (Retrieval-Augmented Generation), prompt engineering, and fine-tuning for use cases like document summarization, query resolution, and task automation.
• Contribute to developing GenAI-powered applications using tools like LangChain, OpenAI APIs, or similar LLM ecosystems.
• Collaborate with engineers to build and test API endpoints, integrate models into applications, and monitor model performance post-deployment.
• Maintain reproducible notebooks and clear documentation for both traditional ML and LLM experiments.
• Stay up to date with trends in machine learning, NLP, and GenAI ecosystems and contribute insights to team discussions.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
• 2.5–3 years of experience in data science, ML, or AI-focused roles (internships or projects count).
• Proficiency in Python, with exposure to libraries such as pandas, NumPy, scikit-learn, and matplotlib.
• Basic experience working with LLMs (e.g., OpenAI, Cohere, Mistral, Hugging Face) or an interest and ability to learn quickly.
• Understanding of NLP fundamentals and vector-based retrieval techniques is a plus.
• Familiarity with SQL and working with structured data sources.
• Clear communication, curiosity, and a willingness to take initiative.

Good-to-Have (Not Mandatory)

• Exposure to building GenAI prototypes using LangChain, LlamaIndex, or similar frameworks.
• Understanding of REST APIs and basics of integrating models into backend systems.
• Experience with cloud platforms (AWS/GCP/Azure), Docker, or Git.

Why Join CoffeeBeans?

• Work on cutting-edge GenAI and ML use cases across fintech, retail, healthcare, and more.
• Fast learning curve with mentorship from senior data scientists, engineers, and architects.
• Flat, collaborative culture that rewards ownership and curiosity.
• Strong emphasis on product thinking, experimentation, and impact.

Overview

Candidate who excels at preparing raw data for analysis and has the expertise to derive actionable features for analytics and machine learning.

Candidate will be involved in the full data lifecycle—from ingestion and cleaning to modeling and feature engineering.

Experience : 5 to 8 Years

Key Responsibilities

• Data Collection & Organization : Gather raw data from diverse sources and structure it into usable formats.
• Data Cleaning : Identify and correct errors, handle missing values, and resolve formatting inconsistencies.
• Data Transformation : Reshape and restructure datasets to meet analytical requirements.
• Data Analysis & Modeling : Apply statistical and machine learning techniques to analyze data and build predictive models.
• Feature Engineering : Design, create, and select meaningful features from raw data to improve model performance and interpretability.
• Experimentation : Design and execute experiments to test hypotheses and measure impact.
• Visualization : Create compelling visualizations and dashboards to communicate findings. (ex. SHAP, Features, Correlation, etc)

Technical Skills

• Proficiency in SQL and Python , for data manipulation, analysis, and automation.
• Experience in Databricks, API ingestion
• Experience in Jupyter, DBeaver, SSMS Dev Tools
• Familiarity in Azure Cloud Platform
• Experience with machine learning libraries (e.g., scikit-learn, etc.).
• Strong knowledge of database management systems and data wrangling tools .
• Familiarity with feature engineering techniques , including encoding, scaling, and dimensionality reduction.

Education & Experience

• Bachelor’s degree in Data Science , Computer Science , Statistics , or a related field.
• Prior experience in data wrangling, feature engineering, and data science roles is highly desirable.

About Danone Group:

Mission: ‘Bringing Health through Food to as Many people as Possible’

Danone is a global leader in food and beverages, focusing on Essential Dairy and Plant-based products, Waters, and Specialized Nutrition. Our mission is to bring health through food to as many people as possible by creating nutritious products, promoting healthy eating habits, and operating sustainably.

With nearly 90,000 employees and products available in over 120 markets, Danone generated €27.6 billion in sales in 2023. Our Renew Danone strategy aims to foster long-term value through innovation and community support.

Danone is committed to making a positive impact economically, socially, and environmentally. In 2020, we became the first listed company to adopt the ‘Société à Mission’ status, reflecting our vision for a sustainable future.

More information can be found at .

About Danone India:

Danone operates in India as Nutricia International Pvt. Ltd, focusing on nutrition with a range of products catering to infants, toddlers, pregnant mothers, as well as adults. The company features well-known brands such as Aptamil, Dexolac, and Protinex. Danone employs over 1,000 individuals across India and generates a turnover exceeding €150 million. The company's head office is located in Mumbai, with a manufacturing facility situated in Lalru, Punjab.

Danone India is a Great Place To Work® certified organization, which reflects our commitment to creating a workplace where people are empowered to contribute meaningfully, grow professionally, and feel a true sense of belonging.

More information can be found at

Job Summary:

Work on the project of transformation of Demand Planning at global level in the roadmap of the digitalization for the Supply Chain. Creating and developing the ML models for all Danone categories

Roles & Responsibilities:

• Be part of the design and development of the ML core models and the analytics behind them
• Understand and capture cross country needs
• Be able to build the common approach of ML scalable models for the demand planning teams of different countries
• Support with all the analytics needs for the adoption of the ML models
• Build outstanding best in class ML models for Demand Planning that are able to cope with more complex and less steady environments
• Set up KPI's to track ML and Statistical models performance
• Select and understand the best approach of automatization for the overall Demand process driven by Statistical and Machine Learning capabilities
• Ensures collaboration of all teams in order to guarantee scalability of the models
• Keeps a close control of the ML developments to ensure cost compliance
• Responsible of the Continuous Improvement of the ML models and create the strategy of ML vs Statistical approach
• Responsible for the region's continuous improvement of ML and Statistical models in order to improve business performance
• Create standard ways of measure and manage strategies to find and fix root causes in forecast bias/accuracy
• Develop capabilities and skills on Machine Learning understanding across the regions
• Build, maintain, fine tune and audit Statistical & ML models to guarantee adaptability to new business context providing service for all regions
• Assist regions in processes and tools to embrace Statistical and ML technology
• Shield key processes and know-how on Statistical and ML
• Ensures standardization between different countries
• Guarantee highest ML utilization

Job Specifications:

• Education: Mathematics/Physics/Engineering with a master’s in business/data analytics or proven track record on Data Science
• Proven track record of minimum 5 years as a data scientist
• Great analytical skills
• Coding capabilities in R and/or Phyton
• Relationship/ Network builder
• Change management
• Project management
• Experience with Continuous Improvement

Main Interfaces

• Cross country demand planning teams
• IS/IT project managers and developers
• Supply chain cross functions

Job Responsibilities of this position :

1. Lead and architect the design and development of AI/ML predictive models that significantly enhance decision-making processes for clients, leveraging their own historical data sets and industry insights.

2. Engage with senior executives and key stakeholders to fully understand their business needs, strategic objectives, and data requirements, ensuring the AI/ML models are precisely tailored to deliver maximum business value.

3. Oversee the implementation, testing, and validation of machine learning algorithms, ensuring that the models are not only accurate but also scalable, reliable, and robust for enterprise-level production environments.

4. Collaborate cross-functionally with product managers, engineers, and data scientists to integrate AI/ML models into Xemplar’s product ecosystem, optimizing performance and efficiency across multiple teams.

5. Provide expert analysis and interpretation of complex data sets from various sources, delivering actionable insights that inform business decisions and improve the accuracy and predictive power of models.

6. Stay ahead of emerging AI/ML trends and innovations, actively incorporating cutting-edge research, techniques, and best practices into the modeling and development processes.

7. Drive the technical vision and strategic direction for AI/ML initiatives, mentoring and guiding less experienced team members while also establishing best practices and a culture of continuous improvement.

8. Partner with business analysts, frontend, mid-tier, and backend developers to ensure the development, deployment, and performance of production applications that effectively utilize AI/ML models to deliver real-world impact.

9. Take ownership of solving complex, ambiguous problems with minimal supervision, applying advanced theoretical knowledge to conceptualize, simulate, and implement AI/ML solutions.

10. Define and manage data requirements and data quality assessments, orchestrating the extraction, transformation, and integration of data for analytical and modeling projects.

Required Relevant Experience:

10+ years of experience in developing and deploying AI/ML predictive models in production environments, with a strong portfolio of successful enterprise-scale projects and solutions.

Expertise in machine learning techniques, including but not limited to regression, classification, clustering, time series analysis, and deep learning.

Advanced proficiency in programming languages such as Python, R, or similar, and mastery of machine learning libraries (e.g., TensorFlow, scikit-learn, PyTorch).

Extensive experience with data wrangling, feature engineering, and working with large, complex, high-volume datasets.

Proven track record in model validation and testing, ensuring models are robust, reliable, and scalable across different use cases and environments.

Deep understanding of the P&C Insurance domain, with specific experience in areas like claims prediction, risk modeling, pricing optimization, and customer segmentation.

Strong ability to communicate complex technical concepts to senior, non-technical stakeholders, influencing decision-making at the highest levels.

Significant hands-on experience working with both structured and unstructured data at scale, with expertise in cloud platforms, distributed computing, and big data technologies.

10+ years of experience in predictive model development, data mining, statistical modeling, and high-performance computing in complex enterprise environments.