Senior Data Scientist (with Generative AI Experience) Location: India (Remote) Company: Process Point Technologies Open Positions: 2 Experience: Minimum 5 years in Data Science (with at least 1–2 years in Generative AI applications) Key Skills Core Data Science : Advanced Statistics, Probability, Hypothesis Testing, and Predictive Modeling. Expertise in Machine Learning & Deep Learning (Regression, Classification, Clustering, Recommendation Systems, NLP). Strong data preparation and feature engineering skills. Programming : Proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow). Generative AI : Recent hands-on experience with LLMs, Prompt Engineering, Fine-tuning, and Retrieval-Augmented Generation (RAG). Frameworks & Tools : Familiarity with LangChain, LangGraph, Hugging Face Transformers. Databases : Experience with Vector Databases (Pinecone, FAISS, Weaviate). Cloud Platforms : Azure (preferred), AWS or GCP exposure a plus. MLOps : Knowledge of model lifecycle management, CI/CD for ML, containerization (Docker/Kubernetes), and API deployment. Data Visualization : Experience with Matplotlib, Seaborn, Power BI, or Tableau. Responsibilities Core Data Science Collect, clean, and analyze structured and unstructured data. Design and deploy ML/DL models for predictive analytics, customer insights, and process optimization. Perform statistical analysis and build data-driven solutions aligned with business needs. Develop dashboards and visual reports for stakeholders. Generative AI Applications Contribute to projects involving LLM-based applications such as chatbots, summarization, and intelligent assistants. Fine-tune pre-trained models for domain-specific requirements. Build RAG pipelines that integrate vector databases for context-aware responses. Apply prompt engineering and domain adaptation techniques to enhance LLM outputs. Integration & Deployment Deploy scalable ML and GenAI solutions using cloud infrastructure. Collaborate with engineering teams to integrate AI solutions with APIs, databases, and business workflows. Innovation & Knowledge Sharing Stay current with emerging trends in Data Science and Generative AI. Document solutions and contribute to team-wide learning. Ensure responsible and ethical AI practices across all deployments.
Senior Data Scientist (with Generative AI Experience) Location: India (Remote) Company: Process Point Technologies Open Positions: 2 Experience: Minimum 5 years in Data Science (with at least 1–2 years in Generative AI applications) Key Skills Core Data Science : Advanced Statistics, Probability, Hypothesis Testing, and Predictive Modeling. Expertise in Machine Learning & Deep Learning (Regression, Classification, Clustering, Recommendation Systems, NLP). Strong data preparation and feature engineering skills. Programming : Proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow). Generative AI : Recent hands-on experience with LLMs, Prompt Engineering, Fine-tuning, and Retrieval-Augmented Generation (RAG). Frameworks & Tools : Familiarity with LangChain, LangGraph, Hugging Face Transformers. Databases : Experience with Vector Databases (Pinecone, FAISS, Weaviate). Cloud Platforms : Azure (preferred), AWS or GCP exposure a plus. MLOps : Knowledge of model lifecycle management, CI/CD for ML, containerization (Docker/Kubernetes), and API deployment. Data Visualization : Experience with Matplotlib, Seaborn, Power BI, or Tableau. Responsibilities Core Data Science Collect, clean, and analyze structured and unstructured data. Design and deploy ML/DL models for predictive analytics, customer insights, and process optimization. Perform statistical analysis and build data-driven solutions aligned with business needs. Develop dashboards and visual reports for stakeholders. Generative AI Applications Contribute to projects involving LLM-based applications such as chatbots, summarization, and intelligent assistants. Fine-tune pre-trained models for domain-specific requirements. Build RAG pipelines that integrate vector databases for context-aware responses. Apply prompt engineering and domain adaptation techniques to enhance LLM outputs. Integration & Deployment Deploy scalable ML and GenAI solutions using cloud infrastructure. Collaborate with engineering teams to integrate AI solutions with APIs, databases, and business workflows. Innovation & Knowledge Sharing Stay current with emerging trends in Data Science and Generative AI. Document solutions and contribute to team-wide learning. Ensure responsible and ethical AI practices across all deployments.
Senior Data Scientist Location: India (Remote) Open Positions: 3 Experience: 5+ years in applied Data Science with production deployments Technical Requirements Core Data Science & Statistics Statistical Foundation: Demonstrated experience with experimental design, A/B testing, causal inference, and time series analysis. Must be able to explain when and why to use different statistical tests. Machine Learning: Hands-on experience building and deploying models for regression, classification, clustering, and forecasting. Should have optimized hyperparameters, handled class imbalance, and dealt with feature drift in production. Deep Learning: Practical experience with neural networks beyond tutorials - custom architectures, loss functions, and optimization strategies for specific business problems. Programming & Engineering Python Proficiency: Advanced usage of core data science stack (Pandas, NumPy, Scikit-learn). Experience with at least one deep learning framework (PyTorch preferred, TensorFlow acceptable). Production Code: Experience writing maintainable, tested code. Understanding of version control, code reviews, and collaborative development practices. Performance Optimization: Experience with profiling, vectorization, and handling datasets that don't fit in memory. LLM Experience (Good to Have) API Integration: Experience integrating with OpenAI, Anthropic, or other LLM APIs in production applications. Prompt Engineering: Systematic approach to prompt design, few-shot learning, and prompt optimization for specific tasks. Fine-tuning & Customization: Experience with parameter-efficient fine-tuning (LoRA, QLoRA) or custom training for domain-specific applications. Responsibilities Collect, clean, and analyze large datasets to derive insights and build predictive solutions. Design and deploy ML/DL models that solve business problems (customer insights, forecasting, optimization). Collaborate with cross-functional teams to integrate AI solutions into business workflows. Build dashboards, reports, and visualizations for business stakeholders. Contribute to projects involving LLM-based applications (chatbots, summarization, assistants) as needed. Stay updated with industry trends and contribute to knowledge sharing within the team.