Data Scientist

Overview

We are a fast-growing startup based in Pune, India, specializing in cutting-edge Data Science and Data Engineering solutions. Our team of dedicated professionals is committed to solving complex data challenges for companies worldwide.

Our Culture

We foster a vibrant startup culture that values:

• Intellectual curiosity
• Continuous learning
• Positive work environment
• Collaborative problem-solving

Role Overview

We are seeking a versatile and proactive Data Scientist to join our dynamic team. The ideal candidate will possess a blend of technical expertise in modern AI/ML technologies, strategic planning, and effective communication skills. This role demands critical thinking, applying data science and problem-solving skills to a wide variety of real-world problems, adaptability to rapidly evolving technologies, and a strong foundation in both traditional and generative AI principles.

Key Responsibilities

• Deliver end-to-end data science projects by applying Machine Learning and Deep Learning fundamentals to solve complex problems
• Derive actionable insights for a variety of problems, industries, and domains using statistical analysis and advanced data science techniques
• Develop high-quality software solutions with Python and other programming languages. Collaborate with developers to understand and improve existing code or create new solutions
• Build and deploy production-ready LLM applications using modern frameworks and best practices
• Design and implement RAG (Retrieval-Augmented Generation) architectures using vector databases and embedding models
• Perform prompt engineering and optimization to maximize LLM performance for specific use cases
• Implement agentic AI systems and multi-agent workflows for complex automation tasks
• Evaluate and benchmark LLM outputs using appropriate metrics and testing frameworks
• Build sophisticated data pipelines for large-scale data processing using modern orchestration tools
• Optimize database performance and create efficient SQL queries
• Deploy and monitor ML models in production using MLOps practices and containerization
• Practice active listening to understand project requirements and team inputs
• Collaborate with clients to translate business requirements into data science solutions
• Communicate complex ideas and results clearly to stakeholders through both verbal and written formats
• Apply responsible AI principles and ensure ethical considerations in model development
• Demonstrate punctuality and a strong sense of ownership in all tasks
• Plan strategically and multitask efficiently to meet project deadlines
• Employ critical thinking to break down problems and debug effectively
• Take initiative and be biased towards action to drive project progress

Required Skills

Core Programming & ML

• Strong Python programming skills with hands-on project experience
• Expertise in Machine Learning and Deep Learning algorithms (Random Forests, GBMs, Neural Networks, CNNs, RNNs, Transformers, Ensemble methods)
• Proficiency in TensorFlow or PyTorch, along with scikit-learn and pandas
• Familiarity with modern ML techniques: Transfer Learning, Few-shot Learning, Self-supervised Learning
• Experience with NLP, Computer Vision, or Time Series Analysis

Generative AI & LLMs

• Hands-on experience with LLM providers (OpenAI, Anthropic Claude, Google Gemini, or open-source models)
• Proficiency with GenAI orchestration frameworks (LangChain, LangGraph, LlamaIndex, or DSPy)
• Experience building RAG applications with vector databases (Pinecone, Weaviate, Chroma, FAISS)
• Strong prompt engineering skills and understanding of prompt optimization techniques
• Knowledge of fine-tuning techniques (LoRA, QLoRA) and when to apply them
• Understanding of LLM evaluation metrics and benchmarking methodologies
• Familiarity with agentic AI architectures and multi-agent systems

MLOps & Deployment

• Experience with MLOps practices and tools (MLflow, Kubeflow, Weights & Biases)
• Proficiency with containerization using Docker and orchestration with Kubernetes
• Experience with cloud platforms (AWS, Azure, or GCP) for ML model deployment and monitoring
• Understanding of CI/CD pipelines for ML applications
• Knowledge of model serving frameworks and API development (FastAPI, Flask, or Django)

Data Engineering & Databases

• Solid understanding of SQL, including advanced concepts like windowing functions and query optimization
• Experience with data pipeline orchestration tools (Airflow, Prefect, or similar)
• Familiarity with both SQL and NoSQL databases

Soft Skills & Professional Attributes

• Strong critical thinking and problem-solving skills
• Excellent written and verbal communication abilities
• Demonstrated ability to work well in a team and independently
• High degree of flexibility and adaptability to rapidly evolving technologies
• Understanding of AI safety principles and responsible AI practices

Nice-to-Have

• Experience with big data technologies (Spark, Hadoop, Databricks)
• Familiarity with BI tools and dashboard creation (Tableau, Power BI, Looker)
• Knowledge of graph databases and knowledge graph construction
• Experience with real-time streaming data processing
• Active participation in data science competitions (Kaggle, DrivenData)
• Contributions to open-source AI/ML projects or technical blog
• Experience with multimodal AI models (vision-language models, audio processing)
• Published research papers or conference presentations

Qualifications

• Data Scientist I: 0-2 years of hands-on experience in Data Science projects
• Data Scientist II: 2-5 years of hands-on experience in Data Science projects
• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related technical field
• Demonstrated commitment to continuous learning through courses, certifications, or self-study (especially in GenAI and modern ML techniques)

What We Offer

• Competitive salary commensurate with experience
• Opportunity to work on diverse, cutting-edge AI/ML projects
• Collaborative and innovation-driven work environment
• Rapid growth and continuous learning opportunities
• Exposure to latest AI technologies and industry best practices