Data Scientist

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.

Key Responsibilities

 Strong understanding of Machine Learning algorithms (supervised and unsupervised)

 Hands-on experience with Deep Learning frameworks (TensorFlow, PyTorch, or similar)

 Experience in NLP techniques and libraries (NLTK, spaCy, Hugging Face, etc.)

 Solid knowledge of Statistics, probability, and data analysis methods

 Proficiency in SQL for querying relational databases

 Strong programming skills in Python (or similar languages)

 Excellent communication skills with the ability to explain complex concepts simply

Required Qualifications

 3+ years of hands-on experience as a Data Scientist or in a similar role.

 Strong expertise in classical machine learning and regression modeling.

 Solid understanding of statistics, including probability, distributions, hypothesis testing,

and correlation analysis.

 Proficiency in Python with libraries such as: scikit-learn pandas NumPy

 Experience working with structured/tabular data.

 Strong problem-solving and analytical thinking skills.

 Ability to clearly explain models and results to non-technical stakeholders.

Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

2

Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

3

Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support.

4

Mandatory (Experience 3) - Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.

5

Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

6

Mandatory (Experience 5) - Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models.

7

Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

8

Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

9

Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

Mandatory (Education) - B.TECH / M.TECH from Tier 1 Colleges (IIT, NIT, BITS, IIIT, DTU, NSUT) are considered.

Roles & Responsibilities

• Design and develop intelligent AI-based applications using advanced NLP and LLM techniques to solve real-world business challenges in financial services.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines leveraging structured and unstructured financial data.
• Integrate and orchestrate LLMs/SLMs for question-answering, summarization, semantic search, and document understanding.
• Develop and maintain RESTful APIs (sync and async) to serve NLP models and chatbot interfaces using frameworks like FastAPI, Flask, etc.
• Should have knowledge of advanced prompting techniques.
• Implement semantic search, hybrid search, and text retrieval systems using Elasticsearch and vector databases (e.g., FAISS, Pinecone, Weaviate).
• Perform NLP tasks such as entity recognition, text classification, intent detection, embedding generation, and sentiment analysis where required.
• Monitor and fine-tune LLM/SLM performance with real-world user data to improve relevance, latency, and accuracy.
• Exposure to LLMOps tools for monitoring, evaluation, and versioning of AI models in production.
• Build, train, and evaluate deep learning models for NLP tasks including classification, NER, summarization, and embedding generation.
• Develop traditional machine learning models (e.g., regression, decision trees, clustering) for structured data analysis and prediction tasks.
• Interact with cross-functional teams to understand system issues and follow up with respective teams to get them fixed.
• Understand and identify areas of improvement across businesses and participate in solution identification and implementation.
• Should be able to work as an Individual Contributor on new and existing projects.
• Positive and problem-solving attitude, must work as an independent contributor.

Ideal Candidate

1.Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

2.Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

3.Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support

4.Mandatory (Experience 3) - Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.

5.Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

6.Mandatory (Experience 5) - Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models

.7.Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

8.Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

9.Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

10.Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

11.Preferred (Experience 1) - Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.

12.Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.

13.Preferred (Experience 3) - Experience working with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.

14.Preferred (Experience 4) - Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture.

15.Preferred (Company) - Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.

16.Mandatory ( Age ) - Candidate Should be Below 28 Years.

17.Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

Hiring for AI Engineer

Exp: 5 - 8 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune

Skill Set:

Total experience ranging from 5 - 8 years in software engineering/AI roles

Min 5 years strong programming experience in Python or Typescript is a MUST

Min 2.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

Experience with cloud platforms (AWS/Azure/GCP)

🚀 Hiring: Python + Gen AI Developer

📍 Location: Bengaluru

💼 Experience: 4–6 Years

Mandatory Skills:

✅ Python

✅ Generative AI / Gen AI

✅ LLMs / AI Application Development

✅ Python Backend / API Development

Role:

Looking for a skilled Python + Gen AI Developer with hands-on experience in developing Python-based applications and integrating Generative AI/LLM solutions.

🔹 Develop scalable Python applications

🔹 Build and integrate Gen AI/LLM solutions

🔹 Develop APIs and backend services

🔹 Collaborate with cross-functional teams

🔹 Troubleshoot and optimize applications

📩 Interested candidates can share their updated resume.

#Hiring #PythonDeveloper #GenAI #GenerativeAI #LLM #AIJobs #BengaluruJobs #TechHiring #ITJobs

🚀 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

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

We are looking for a Data Science & Machine Learning Senior Associate with 3–5 years of relevant experience in data science, machine learning, and analytics. The candidate will be responsible for developing predictive models, analyzing complex datasets, building scalable ML solutions, and supporting production-grade data science applications on cloud platforms.

• Develop and implement machine learning and predictive analytics models.
• Perform data analysis, statistical modeling, and optimization to solve business problems.
• Build demand forecasting and predictive models using time-series and other advanced techniques.
• Work with large datasets using Python, SQL, PySpark, and cloud-based data platforms.
• Develop and maintain scalable data pipelines for ML model development and deployment.
• Implement MLOps practices including model deployment, monitoring, retraining, and data-drift detection.
• Collaborate with software engineers, product teams, and business stakeholders to convert business requirements into analytical solutions.
• Validate models for accuracy, robustness, bias, and production readiness.
• Create meaningful visualizations and communicate analytical insights to technical and non-technical stakeholders.

• Python
• SQL
• Data Science & Machine Learning
• Predictive Modeling
• Statistical Analysis
• Machine Learning Algorithms
• Optimization Techniques
• Google Cloud Platform (GCP)
• BigQuery
• Dataflow
• Dataproc
• Data Fusion
• Cloud SQL
• Airflow
• PySpark
• PostgreSQL
• Terraform
• Tekton
• APIs
• MLOps

• Java

• Demand Forecasting
• Time-Series Analysis
• Neural Networks
• Ensemble Methods
• Support Vector Machines (SVM)
• Regression and Cluster Analysis
• ML Model Testing
• Bias Detection and Data Drift Monitoring
• Production ML Deployment
• QlikSense
• Automotive or Supply Chain Analytics

Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related technical field.Master's degree in a relevant quantitative or technical field is preferred.

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

2

Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

3

Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design

4

Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering)

5

Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

6

Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale

7

Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

8

Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes)

9

Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

10

Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

11

Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

12

Mandatory (Company): B2B Tech/Consulting/B2B Software