Data Scientist II

About the role

As a Data Scientist, you will focus on developing and implementing advanced data science and machine learning models to support our clients business needs. Youll be solving real world business problems for clients globally using predictive analytics, NLP, and Computer Vision. On a typical day, you’ll find yourself working across the end-to-end machine learning lifecycle - from data preparation to model training and deployment. This is a role which prioritizes ownership, excellence, and collaboration.

Apply at: https://app.fabrichq.ai/jobs/1e0c79b7-cac4-4047-b664-0601d5b35029/

About the company

Aidetic is an AI/ML consulting company working at the cutting edge of AI since 2018, before it was cool. We’re a small but highly talented team of 50 people passionate about solving real world problems with AI. With clients such as Target, Tata Steel, InsuranceDekho etc. we have put 200+ AI systems in production.

Expected Compensation

Upto 20 Lakhs Annually

Experience

2 to 4 years of data science experience

Key Responsibilities

• Data Science & Model Development: Design and implement machine learning and deep learning models, utilizing both supervised and unsupervised techniques to address complex business challenges.
• Data Preparation & Feature Engineering: Conduct data exploration, preprocessing, and feature engineering to create high-quality datasets and features that enhance model effectiveness.
• Advanced Optimization & Evaluation: Apply advanced techniques for hyper-parameter tuning and assess model performance using appropriate evaluation metrics, ensuring model robustness.
• Generative AI: Work with developing LLM powered solutions using OpenAI, Claude, or open source LLMs like Llama. Use frameworks like Langchain, Llama Index etc. to develop RAG powered chatbots. Use frameworks such as Crew AI, Autogen etc. to develop agentic workflows.
• MLOps & Deployment: Contribute to the deployment of models with MLOps practices, including versioning, pipeline orchestration, and containerization, ensuring scalability and efficiency.
• Collaboration & Communication: Partner with cross-functional teams, including data engineering and business stakeholders, to ensure alignment with client needs and communicate findings effectively.
• API Development & Integration: Use RESTful API frameworks to develop, integrate, and scale data-driven applications within client environments.
• Continuous Learning & Improvement: Stay current with new data science techniques and industry trends, sharing knowledge with the team and contributing to a culture of innovation.

Qualifications

Essential

• Qualification: Bachelor's or Master's degree in a relevant field (Computer Science, Data Science, Statistics etc.).
• Experience: 2+ years of experience in data science and machine learning. ● Programming & Tools: Proficient in Python, Git, and coding IDEs like VS Code or PyCharm.
• Machine Learning & Deep Learning: Strong knowledge of traditional machine learning algorithms, deep learning, and advanced hyper-parameter tuning.
• Data Manipulation & Analysis: Expertise in data exploration, preprocessing, statistical analysis, feature engineering, and model evaluation.
• MLOps & Deployment: Experience with MLOps basics, containerization tools (Docker, Kubernetes), and cloud platforms (AWS, Azure, Google Cloud).
• GenAI, RAG and AI Agent Frameworks: Proficiency with NLP techniques and familiarity with RAG frameworks like LangChain, LlamaIndex, or CrewAI, Autogen. ● Databases: Familiarity with SQL and NoSQL databases
• Soft Skills: Strong analytical thinking, self-motivation, effective communication, and a high degree of ownership over projects.

Good to Know

• LLM Finetuning and Quantization
• Edge Deployment
• Big Data Technologies: Experience with PySpark, Hadoop, etc.
• Graph Databases: Knowledge of Neo4j, RDF, or similar technologies. ● Messaging Queues: Familiarity with messaging queue systems.Certifications in Databricks Engineer Professional or Microsoft Azure Data Engineer.