Data Science Engineer-MNC Company-Chennai

Job Summary:

We are looking for a skilled Data Scientist with strong expertise in demand forecasting, predictive analytics, and emerging Generative AI technologies. The ideal candidate should have hands-on experience in machine learning, deep learning, NLP, and LLM-based solutions, along with proficiency in Python, SQL, Power BI, and advanced Excel. This role involves building scalable forecasting models and leveraging AI/GenAI to deliver actionable business insights.

Key Responsibilities:

• Develop and deploy demand forecasting models using machine learning and deep learning techniques.
• Analyze historical data to identify trends, seasonality, and demand patterns.
• Build predictive models to improve supply chain and inventory planning.
• Work with large datasets using Python and SQL for data extraction, transformation, and analysis.
• Design dashboards and reports using Power BI for business stakeholders.
• Utilize advanced Excel techniques (Pivot Tables, Power Query, formulas) for analysis and reporting.
• Build and integrate NLP-based solutions for text data analysis and insights.
• Develop and implement LLM-based applications using Generative AI frameworks.
• Design and deploy RAG (Retrieval-Augmented Generation) pipelines for intelligent data retrieval and response generation.
• Collaborate with cross-functional teams (operations, finance, product) to align forecasting and AI solutions.
• Continuously improve model accuracy and performance through experimentation and optimization.

Required Skills:

• Strong proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
• Solid understanding of machine learning & deep learning algorithms.
• Experience in demand forecasting / time-series analysis (ARIMA, Prophet, LSTM, etc.).
• Hands-on experience with NLP techniques and libraries (NLTK, SpaCy, Transformers).
• Experience working with LLMs and Generative AI frameworks (OpenAI, Hugging Face, LangChain, etc.).
• Strong understanding of RAG architectures and vector databases (FAISS, Pinecone, etc.).
• Advanced knowledge of SQL for data manipulation.
• Hands-on experience with Power BI for visualization and reporting.
• Expertise in advanced Excel (Power Query, dashboards, data modeling).
• Strong analytical and problem-solving skills. 

Preferred Qualifications:

• Experience in supply chain, logistics, or e-commerce forecasting.
• Knowledge of cloud platforms (AWS, Azure, or GCP).
• Familiarity with data pipelines and ETL processes.
• Understanding of business metrics and KPIs related to demand planning.

Position Overview:

We are building a new retail merchandising platform on top of that data. It answers what to stock, how much,

and in which store—combining sales, catalogue, and customer data with GeoIQ’s location attributes at retail

scale: thousands of stores, tens of thousands of SKUs, decisions made weekly.

As part of the data science team on this platform, you will build the forecasting, assortment and

recommendation models behind those decisions. The platform is new, so the models are being built now, not

maintained. Hands-on modelling with senior review — what you ship changes what a store actually orders.

Responsibilities:

● Build SKU- and store-level demand forecasts, including for new products with little or no sales history.

● Build assortment models — which products earn shelf space in which stores or store clusters, and how

a range change is likely to play out.

● Build recommendation and ranking models for product discovery, cross-sell and bundling, including

cold-start for new products and customers.

● Engineer features from sales, seasonality, promotions, pricing, catalogue hierarchy, store attributes and

GeoIQ’s location and demographic layers.

● Handle the realities of retail data: intermittent demand, stockouts censoring true demand, returns and

cannibalisation between similar SKUs.

● Design backtests and A/B tests that reflect how planners use the output; monitor deployed models for

drift.

● Build data pipelines and work with engineering to take models to production.

● Communicate findings to merchandising, category, and product teams.

Expectations:

● Proficient in Python

● Work experience: 1–3 years

● Prior experience with building ML models end-to-end

● Prior experience with SQL

● Knowledge of statistical techniques and model evaluation

● Experience with time-series forecasting or recommender systems will be an added advantage

● Experience with retail, e-commerce, supply chain, or spatial data will be an added advantage

Join us early and help build the merchandising brain for retail at national scale — close to the product and the

customer, with the freedom of a platform still being shaped.

🚀 We’re Hiring | Data Scientist 🧠📊

Ready to turn data into real-world intelligence? Join us and work on exciting AI/ML & data-driven solutions!

🔹 Experience: 8+ Years

🔹 Must-Have Skills:

🐍 Python | 🤖 Machine Learning | ☁️ Cloud | 🧠 NLP | 📊 Data Visualization

📍 Location: Pune

💼 Work Mode: Work from Office

If you're passionate about Data Science, AI & solving complex business problems, we’d love to hear from you!

📩 Interested? Kindly text

#Hiring #DataScientist #DataScience #MachineLearning #Python #NLP #AI #Cloud #DataVisualization #TechJobs #HiringNow

Role Overview 

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact. 

Key Responsibilities 

Data Science & Machine Learning 

• Analyze structured and unstructured data to identify patterns, trends, and business opportunities.  
• Perform exploratory data analysis (EDA), feature engineering, and data preparation.  
• Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.  
• Apply statistical techniques to solve business problems and validate model performance.  
• Design and execute experiments to improve model accuracy and business outcomes.  

 AI Solution Development 

• Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.  
• Translate business requirements into scalable data science approaches.  
• Contribute to Generative AI and advanced analytics initiatives where applicable.  
• Document methodologies, model performance, and key findings.  

 Required Technical Skills 

• Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.  
• Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.  
• Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.  
• Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.  
• Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.  
• Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.  
• Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.  
• Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.  
• Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred. 

Preferred Qualifications 

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.  
• 2–4 years of experience developing machine learning or data science solutions.  
• Experience working on end-to-end data science projects in a business environment.  

 Nice to Have 

• Exposure to Generative AI, LLMs, RAG, or Agentic AI.  
• Experience with Computer Vision or Natural Language Processing (NLP).  
• Familiarity with cloud-based AI platforms.  
• Knowledge of construction, engineering, manufacturing, or industrial domains.  
• Participation in hackathons, research, Kaggle competitions, or open-source projects.  

 Soft Skills 

Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.

Data Scientist – Machine Learning & Applied Analytics. 

Location: Bengaluru 

Experience: 5–8 Years 

Role Type: Full-Time 

Business Unit: Products & Platforms (PMG) 

Role Overview 

We are looking for a Data Scientist – Product Analytics & Machine Learning to help build differentiated analytics and ML capabilities across our product and platform portfolio, including MOBIUS+ and Signate. 

This is a hands-on product-focused role responsible for translating real-world customer and domain problems into scalable analytics and machine learning solutions. The role will work across the complete lifecycle — from data exploration and experimentation through model development, validation, productization and production monitoring. 

The Data Scientist will also work closely with Tata Elxsi's internal analytics teams, specialist solution providers and academic/research institutions to evaluate, integrate and evolve analytical capabilities as part of our products. 

Key Responsibilities 

Product Analytics & ML 

• Translate product and customer problems into well-defined analytics and ML use cases. 
• Perform data exploration, feature engineering, statistical analysis and experimentation. 
• Develop, train, validate and optimize machine learning and predictive models. 
• Define appropriate model performance metrics, benchmarks and acceptance criteria. 
• Convert prototypes and experiments into scalable, production-ready capabilities. 
• Work with Product, Architecture and Engineering teams to integrate models and analytics into SaaS products and platforms. 

Model Evaluation & Productization 

• Evaluate models and analytical solutions developed by internal teams, technology partners and specialist solution providers. 
• Assess model assumptions, data requirements, accuracy, robustness, explainability and limitations. 
• Benchmark alternative models/approaches and provide evidence-based recommendations. 
• Establish practices for model versioning, validation, monitoring, drift detection and continuous improvement. 
• Ensure that externally sourced models can be effectively integrated, governed and evolved within the product. 

Collaboration & Research 

• Collaborate with Tata Elxsi's broader AI, analytics and domain teams and leverage specialist capabilities where required. 
• Work technically with external solution providers to understand and integrate their algorithms and models. 
• Engage with researchers and academic institutions such as IITs, NITs and IISc on advanced analytics and ML opportunities. 
• Evaluate research, algorithms and PoCs and help translate promising approaches into production-grade product capabilities. 
• Build reusable analytical assets, datasets, features, evaluation frameworks and know-how across the product portfolio. 

Technical Skills 

• Strong foundation in statistics, probability, machine learning and data analysis. 
• Strong hands-on programming skills in Python and common data science libraries. 
• Experience with Pandas, NumPy, scikit-learn and relevant ML frameworks. 
• Experience developing predictive models using structured and time-series data. 
• Understanding of supervised/unsupervised learning, anomaly detection, forecasting, classification and regression techniques. 
• Experience with feature engineering, model selection, hyperparameter optimization and model evaluation. 
• Good understanding of model explainability, bias, uncertainty and robustness. 
• Working knowledge of SQL and large-scale data processing. 
• Experience deploying or integrating ML models into production applications through APIs, services or ML pipelines. 
• Understanding of MLOps, including model versioning, experiment tracking, monitoring and model lifecycle management. 
• Experience with cloud environments, preferably AWS, is desirable. 
• Exposure to GenAI/LLMs and their application within product analytics is an advantage. 

Product & Engineering Mindset 

• Ability to move beyond experimentation and think about how analytics becomes a repeatable product capability. 
• Strong problem-solving ability and comfort working with incomplete or imperfect real-world datasets. 
• Ability to distinguish between an interesting analytical result and one that creates measurable customer/product value. 
• Comfortable working closely with Product Managers, Domain SMEs, Architects and Software Engineers. 
• Ability to critically evaluate third-party algorithms and models rather than treating external solutions as black boxes. 
• Strong technical communication skills and ability to explain analytical concepts and model outcomes to both technical and business stakeholders. 

Preferred Experience 

• 5–8 years of relevant experience in Data Science, Machine Learning, Applied Analytics or related roles. 
• Experience building ML/analytics capabilities that have been deployed into production products or platforms. 
• Experience working with automotive, mobility, connected vehicle, battery/EV, IoT or industrial datasets is desirable but not mandatory. 
• Experience collaborating with technology partners, research organizations or academic institutions is an advantage. 
• Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related quantitative discipline preferred. 

What Success Looks Like 

The successful candidate will help PMG establish a strong internal Data Science & ML capability that can build differentiated analytics, intelligently leverage Tata Elxsi's broader capabilities and external specialists, and retain the knowledge and ownership required to continuously evolve analytics as a core part of our products and platforms. 

Job Description – Data Scientist (Machine Learning & Forecasting)

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, Traditional Statistical Modelling, Forecasting, and Predictive Analytics. The ideal candidate will have hands-on experience building and deploying end-to-end ML solutions, working with large datasets, and translating business problems into scalable data science solutions.

The role requires a strong foundation in statistics, predictive modelling, feature engineering, model evaluation, and time-series forecasting, along with the ability to collaborate with cross-functional teams to deliver business impact.

Key Responsibilities

• Design, develop, and deploy Machine Learning models for business-critical use cases.
• Build and optimize traditional ML models such as:
• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting (XGBoost, LightGBM, CatBoost)
• Support Vector Machines
• Clustering Algorithms
• Develop forecasting solutions using:
• ARIMA / SARIMA
• Prophet
• Exponential Smoothing
• Time-Series Regression Models
• Perform exploratory data analysis (EDA), feature engineering, and data validation.
• Evaluate model performance using appropriate statistical and business metrics.
• Work with structured and semi-structured datasets from multiple sources.
• Collaborate with business stakeholders to understand requirements and translate them into analytical solutions.
• Build scalable data pipelines and support model deployment in production environments.
• Monitor model performance, identify data drift, and implement model retraining strategies.
• Present insights and recommendations to technical and non-technical stakeholders.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field.
• 5+ years of hands-on experience in Data Science, Machine Learning, and Forecasting.

Technical Skills

   Machine Learning

• Strong understanding of supervised and unsupervised learning algorithms.
• Experience with ensemble methods and advanced ML techniques.
• Expertise in model selection, hyperparameter tuning, and performance optimization.

Forecasting & Statistics

• Strong understanding of:
• Time-Series Analysis
• Forecasting Techniques
• Statistical Inference
• Hypothesis Testing
• Probability Distributions
• A/B Testing

Programming

• Advanced proficiency in Python.
• Experience with:
• Pandas
• NumPy
• Scikit-learn
• Statsmodels
• XGBoost / LightGBM
• Prophet

Data & SQL

• Strong SQL skills with experience in complex queries and performance optimization.
• Experience working with large-scale datasets.

Visualization

• Experience with Power BI, Tableau, Matplotlib, Seaborn, or Plotly.
• Cloud & MLOps (Preferred)
• Exposure to AWS, Azure, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and ML model deployment practices.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work independently in a fast-paced environment.
• Strong business acumen and data-driven decision-making mindset. 

Data Science Engineer – Python, SQL, Statistics & Machine Learning

Company: Talview

Experience: 3–5 years

Location: Bengaluru, Karnataka

Work Mode: Work from Office, with some flexibility

Work Week: 5 days

About Talview

Talview builds AI-powered hiring and assessment solutions for enterprise organisations. Our products support candidate screening, interview scheduling and online exam proctoring, helping teams improve efficiency and make better-informed decisions.

Role Overview

We are looking for a Data Science Engineer with strong hands-on experience in Python, SQL, statistics and classical machine learning, along with experience working on GenAI and Large Language Models (LLMs).

You will work across data processing, statistical analysis, experimentation, model development, evaluation and production AI/ML workflows. You will collaborate with Engineering and Product teams to translate business problems into measurable data science solutions.

This role requires practical experience in classical ML and statistics, beyond working only with LLM APIs or prompt engineering.

Roles and Responsibilities

• Develop complex SQL queries for data extraction, transformation, analysis and validation.
• Use Python for data processing, exploratory data analysis, automation and reusable experimentation utilities.
• Apply statistical analysis, hypothesis testing and experimental design to evaluate business and product outcomes.
• Develop and evaluate machine learning models using appropriate metrics and clearly defined success criteria.
• Build LLM workflows involving prompting, structured outputs and comparative evaluation of models and configurations.
• Create evaluation datasets, benchmarks, sampling strategies and ground-truth validation approaches.
• Perform error analysis and root-cause analysis to identify model failures, anomalies and performance degradation.
• Analyse production data and troubleshoot data, model and pipeline issues.
• Integrate data from APIs, databases and other structured and unstructured sources.
• Collaborate with Engineering and Product teams on ML pipelines, evaluation frameworks and inference workflows.
• Research and test techniques to improve AI/ML system performance.

Required Skills and Experience

• 3–5 years of relevant experience in Data Science, Machine Learning, AI or a related field.
• Strong hands-on Python programming skills.
• Strong SQL skills for complex querying, data transformation and analysis.
• Solid understanding of statistics, hypothesis testing, experiment design and performance measurement.
• Practical experience developing and evaluating classical machine learning models.
• Experience with LLMs, prompting, structured outputs and evaluation.
• Experience defining evaluation metrics, benchmarks and error-analysis approaches.
• Ability to work with APIs, databases and structured and unstructured datasets.
• Strong analytical and problem-solving skills.
• Ability to explain technical decisions, interpret results and communicate findings to stakeholders.

Preferred Skills

• Experience designing end-to-end ML or LLM workflows.
• Experience with dataset creation, sampling, annotation and ground-truth validation.
• Experience analysing production AI/ML systems and debugging model or pipeline issues.
• Experience building reusable experimentation and evaluation frameworks.

Key Skills

Python, SQL, Data Science, Statistics, Machine Learning, Classical ML, Hypothesis Testing, Experimental Design, Model Evaluation, Exploratory Data Analysis, GenAI, Large Language Models, LLM Evaluation, Error Analysis

We are looking for a hands-on Lead Data Scientist with strong analytical, machine learning, and problem-solving skills to work in a client-facing environment.

The role requires someone who can independently identify opportunities, formulate hypotheses, design solutions, and drive initiatives from analysis through experimentation and production implementation. The candidate should also be comfortable guiding team members and working closely with engineering and client stakeholders.

Responsibilities: 

• Analyse complex datasets to identify patterns, issues, and opportunities.
• Formulate and validate hypotheses through structured analysis and experimentation.
• Build and improve machine learning and anomaly detection solutions.
• Design end-to-end solutions considering data, modelling, engineering, and production constraints.
• Perform root cause analysis across models, data, and systems.
• Work with engineering teams on feature pipelines, model inference, and production deployment.
• Lead technical discussions with clients and communicate recommendations, trade-offs, and expected impact.
• Guide team members on analysis, modelling, and solution design.
• Proactively identify initiatives and roadmap items that can add value to the project.

Requirements: 

• Strong foundation in statistics and machine learning.
• Strong hands-on experience with Python and SQL.
• Hands-on experience with Python ML and data frameworks such as Pandas, NumPy, scikit-learn, TensorFlow/Keras, Dask, Matplotlib, Boto3 SageMaker Python SDK, and Horovod.
• Strong data analysis, hypothesis-generation, and problem-solving skills.
• Experience with standard supervised and unsupervised ML techniques.
• Experience with anomaly detection techniques such as Isolation Forest and Autoencoders.
• Experience building and deploying production ML solutions.
• Working knowledge of data engineering and real-time / batch inference environments.
• Strong communication and stakeholder-management skills.
• Ability to lead technical work and guide cross-functional teams.

Good to Have: 

• Experience in fraud detection or risk modelling.
• Experience with Graph Neural Networks.
• Exposure to real-time systems, streaming features, and low-latency data stores.
• Experience in AdTech, e-commerce, gaming, mobile applications, or similar high-volume consumer platforms.