Data Scientist

About Us:

Solytics Partners is a Global Analytics firm, recognized with multiple industry awards for innovation and excellence. Our team comprises experts with deep domain knowledge in risk, analytics, AI/ML, AML/FCC, and fraud. By converging this expertise with cutting-edge technologies like AI, Machine Learning, Generative AI, and Large Language Models (LLMs), we deliver powerful automated platforms and incisive point solutions.

Our offerings enable clients to streamline and future-proof their risk, AML, and analytics processes, comply seamlessly with global regulations, and safeguard financial systems. Whether it's solving complex challenges or driving operational efficiency, Solytics Partners is committed to empowering organizations with transformative tools to stay ahead in an evolving regulatory landscape.

Job Summary:

We are looking for a hands-on and technically strong Associate Data Scientist with 1.6–2 years of experience to join the product development team of our client. The ideal candidate should have a strong foundation in Python, Machine Learning, Natural Language Processing (NLP), and Generative AI, along with practical experience in developing AI-driven applications.

In this role, you will contribute to designing, developing, integrating, and optimizing Machine Learning and Generative AI solutions for real-world product use cases. You will work with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), NLP techniques, and modern AI frameworks to build scalable, reliable, and production-ready product features.

The role requires strong analytical and problem-solving skills, good software engineering practices, and the ability to collaborate with cross-functional teams to translate product requirements into effective AI solutions. You will have opportunities to work across the AI development lifecycle, from data exploration and model experimentation to application integration, deployment, and continuous improvement.

Key Responsibilities

• Machine Learning & Model Development: Develop, train, evaluate, and optimize Machine Learning and Deep Learning models for real-world product use cases, including classification, prediction, and NLP-based applications.
• Generative AI & LLM Applications: Build and integrate LLM-powered applications using models such as GPT, Claude, Gemini, and open-source LLMs to address specific product requirements.
• RAG Development: Design, develop, and enhance Retrieval-Augmented Generation (RAG) pipelines, including document processing, text chunking, embedding generation, semantic search, information retrieval, and contextual response generation.
• Python Development: Write clean, modular, reusable, and maintainable Python code while following software engineering best practices and established coding standards.
• NLP Solutions: Develop and implement NLP-based solutions involving text preprocessing, tokenization, text classification, named entity recognition, information extraction, and summarization.
• Data Processing & Analysis: Perform data extraction, cleaning, preprocessing, exploratory data analysis, and feature engineering using Python, Pandas, NumPy, and SQL to support model development.
• Model Evaluation & Optimization: Evaluate model performance using appropriate metrics, conduct experiments, perform error analysis, and implement improvements to enhance model accuracy, efficiency, and reliability.
• Product Feature Development: Contribute to the development, integration, testing, and enhancement of AI-powered features within the client's products, ensuring they meet functional and technical requirements.
• API Development & Integration: Develop and integrate AI/ML solutions with product applications using REST APIs and frameworks such as FastAPI.
• AI Application Engineering: Work with modern AI frameworks and libraries such as LangChain, LangGraph, and Hugging Face to develop and integrate intelligent applications.
• Deployment & MLOps: Support model packaging, deployment, versioning, monitoring, and lifecycle management using tools such as Docker, MLflow, and cloud-based ML platforms.
• Vector Search & Knowledge Retrieval: Work with embedding-based retrieval systems and vector databases to implement semantic search, document retrieval, and knowledge-based AI applications.
• Testing & Debugging: Perform unit testing, troubleshoot technical issues, debug model and application behavior, and contribute to improving the stability and reliability of AI-powered features.
• Cross-functional Collaboration: Work closely with Data Scientists, Software Engineers, Product Managers, and other stakeholders to understand requirements, discuss technical approaches, and deliver effective solutions.
• Continuous Improvement: Stay updated with emerging developments in Machine Learning, Generative AI, LLMs, NLP, and AI engineering practices, and explore ways to incorporate relevant technologies into product development.

Required Skills & Qualifications

• 1.6–2 years of hands-on experience in Data Science, Machine Learning, NLP, or Applied AI development.
• Strong programming skills in Python, including object-oriented programming, data structures, modular development, and writing reusable code.
• Good understanding of fundamental Machine Learning algorithms, model training, validation, evaluation, and optimization techniques.
• Practical experience with Machine Learning libraries and frameworks such as Scikit-learn, PyTorch, or TensorFlow.
• Hands-on exposure to Generative AI and Large Language Models (LLMs), including prompt engineering, LLM integration, and building AI-powered applications.
• Understanding of Retrieval-Augmented Generation (RAG) architectures, including embeddings, chunking strategies, retrieval mechanisms, and contextual response generation.
• Good understanding of Natural Language Processing (NLP) concepts, including text preprocessing, tokenization, text classification, named entity recognition, and text summarization.
• Proficiency in data manipulation and analysis using Pandas, NumPy, and SQL.
• Exposure to GenAI frameworks and libraries such as LangChain, LangGraph, Hugging Face, or similar tools.
• Familiarity with REST APIs and experience developing or integrating AI applications using FastAPI or similar frameworks.
• Basic hands-on knowledge of Git, Docker, and software development practices.
• Understanding of model evaluation metrics, experimentation, data quality, and techniques for improving model performance.
• Familiarity with vector databases such as FAISS, Chroma, Pinecone, or Weaviate.
• Understanding of the challenges involved in integrating and deploying AI/ML solutions in real-world applications.
• Strong analytical thinking, problem-solving, debugging, and communication skills.
• Ability to work collaboratively in a fast-paced product development environment and take ownership of assigned technical tasks

Good-to-have

• Experience working with AI Agents, agentic workflows, tool calling, or multi-step LLM applications.
• Exposure to vector databases such as FAISS, Chroma, Pinecone, or Weaviate and experience implementing semantic search or retrieval-based applications.
• Familiarity with cloud platforms such as Microsoft Azure or AWS, including Azure ML, Azure Blob Storage, or Amazon S3.
• Basic understanding of MLOps practices and tools such as MLflow, Kubeflow, or Azure ML.
• Experience deploying or integrating ML/GenAI applications using Docker and cloud environments.
• Understanding of Deep Learning architectures, Transformers, and model fine-tuning techniques.
• Exposure to data pipelines, ETL processes, and tools such as Airflow or Databricks.
• Experience working with structured, unstructured, or multilingual datasets.
• Familiarity with microservices, API integration, and real-time AI applications.
• Contributions to open-source AI/ML projects, technical blogs, research, or relevant personal projects

What We Are Looking For

• A technically curious and hands-on Associate Data Scientist with strong Python programming and Machine Learning fundamentals.
• Candidates with practical experience in developing ML, NLP, or Generative AI applications and the ability to explain their implementation and technical decisions.
• A product-oriented mindset, with an understanding of how AI solutions can be developed into reliable, maintainable, and scalable product features.
• The ability to move beyond experimentation and contribute to application development, integration, testing, and deployment.
• Strong problem-solving skills, attention to code quality, and a willingness to learn and adopt new technologies.
• A collaborative team player who can work with engineering and product teams, understand requirements, and deliver solutions within defined timelines.
• An individual who takes ownership of assigned tasks, proactively identifies issues, and continuously works toward improving technical skills and product outcomes.