{"id":1944089,"url":"https://alion.io/job/solytics-partners-data-scientist","title":"Data Scientist","company":{"id":175323,"name":"Solytics Partners","domain":"solytics-partners.com","url":"https://alion.io/company/solytics-partners","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":11500,"max_usd":27000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":12},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Chroma","optional":false},{"name":"Claude","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"Hugging Face","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Tokenization","optional":false},{"name":"Weaviate","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"AI Agents","optional":true},{"name":"Amazon S3","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Databricks","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Function Calling","optional":true},{"name":"Kubeflow","optional":true},{"name":"Tool Use","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-10-06T03:09:03Z","employer_posted_date":null,"last_verified_at":"2026-10-06T03:09:03Z","board_verified":false,"closed_at":null,"days_open":9,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":9},"description":"About Us:\nSolytics 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.\nOur 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.\n\nJob Summary:\nWe 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.\n\nIn 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.\n\nThe 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.\n\nKey Responsibilities\nMachine 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.\nGenerative 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.\nRAG 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.\nPython Development: Write clean, modular, reusable, and maintainable Python code while following software engineering best practices and established coding standards.\nNLP Solutions: Develop and implement NLP-based solutions involving text preprocessing, tokenization, text classification, named entity recognition, information extraction, and summarization.\nData Processing & Analysis: Perform data extraction, cleaning, preprocessing, exploratory data analysis, and feature engineering using Python, Pandas, NumPy, and SQL to support model development.\nModel Evaluation & Optimization: Evaluate model performance using appropriate metrics, conduct experiments, perform error analysis, and implement improvements to enhance model accuracy, efficiency, and reliability.\nProduct 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.\nAPI Development & Integration: Develop and integrate AI/ML solutions with product applications using REST APIs and frameworks such as FastAPI.\nAI Application Engineering: Work with modern AI frameworks and libraries such as LangChain, LangGraph, and Hugging Face to develop and integrate intelligent applications.\nDeployment & MLOps: Support model packaging, deployment, versioning, monitoring, and lifecycle management using tools such as Docker, MLflow, and cloud-based ML platforms.\nVector Search & Knowledge Retrieval: Work with embedding-based retrieval systems and vector databases to implement semantic search, document retrieval, and knowledge-based AI applications.\nTesting & Debugging: Perform unit testing, troubleshoot technical issues, debug model and application behavior, and contribute to improving the stability and reliability of AI-powered features.\nCross-functional Collaboration: Work closely with Data Scientists, Software Engineers, Product Managers, and other stakeholders to understand requirements, discuss technical approaches, and deliver effective solutions.\nContinuous 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.\n\nRequired Skills & Qualifications\n1.6–2 years of hands-on experience in Data Science, Machine Learning, NLP, or Applied AI development.\nStrong programming skills in Python, including object-oriented programming, data structures, modular development, and writing reusable code.\nGood understanding of fundamental Machine Learning algorithms, model training, validation, evaluation, and optimization techniques.\nPractical experience with Machine Learning libraries and frameworks such as Scikit-learn, PyTorch, or TensorFlow.\nHands-on exposure to Generative AI and Large Language Models (LLMs), including prompt engineering, LLM integration, and building AI-powered applications.\nUnderstanding of Retrieval-Augmented Generation (RAG) architectures, including embeddings, chunking strategies, retrieval mechanisms, and contextual response generation.\nGood understanding of Natural Language Processing (NLP) concepts, including text preprocessing, tokenization, text classification, named entity recognition, and text summarization.\nProficiency in data manipulation and analysis using Pandas, NumPy, and SQL.\nExposure to GenAI frameworks and libraries such as LangChain, LangGraph, Hugging Face, or similar tools.\nFamiliarity with REST APIs and experience developing or integrating AI applications using FastAPI or similar frameworks.\nBasic hands-on knowledge of Git, Docker, and software development practices.\nUnderstanding of model evaluation metrics, experimentation, data quality, and techniques for improving model performance.\nFamiliarity with vector databases such as FAISS, Chroma, Pinecone, or Weaviate.\nUnderstanding of the challenges involved in integrating and deploying AI/ML solutions in real-world applications.\nStrong analytical thinking, problem-solving, debugging, and communication skills.\nAbility to work collaboratively in a fast-paced product development environment and take ownership of assigned technical tasks\n\nGood-to-have\nExperience working with AI Agents, agentic workflows, tool calling, or multi-step LLM applications.\nExposure to vector databases such as FAISS, Chroma, Pinecone, or Weaviate and experience implementing semantic search or retrieval-based applications.\nFamiliarity with cloud platforms such as Microsoft Azure or AWS, including Azure ML, Azure Blob Storage, or Amazon S3.\nBasic understanding of MLOps practices and tools such as MLflow, Kubeflow, or Azure ML.\nExperience deploying or integrating ML/GenAI applications using Docker and cloud environments.\nUnderstanding of Deep Learning architectures, Transformers, and model fine-tuning techniques.\nExposure to data pipelines, ETL processes, and tools such as Airflow or Databricks.\nExperience working with structured, unstructured, or multilingual datasets.\nFamiliarity with microservices, API integration, and real-time AI applications.\nContributions to open-source AI/ML projects, technical blogs, research, or relevant personal projects\n\nWhat We Are Looking For\nA technically curious and hands-on Associate Data Scientist with strong Python programming and Machine Learning fundamentals.\nCandidates with practical experience in developing ML, NLP, or Generative AI applications and the ability to explain their implementation and technical decisions.\nA product-oriented mindset, with an understanding of how AI solutions can be developed into reliable, maintainable, and scalable product features.\nThe ability to move beyond experimentation and contribute to application development, integration, testing, and deployment.\nStrong problem-solving skills, attention to code quality, and a willingness to learn and adopt new technologies.\nA collaborative team player who can work with engineering and product teams, understand requirements, and deliver solutions within defined timelines.\nAn individual who takes ownership of assigned tasks, proactively identifies issues, and continuously works toward improving technical skills and product outcomes.","description_format":"text","description_chars":8876,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["RegTech, AML & Compliance"],"lifecycle":[{"event":"open","at":"2026-10-06T05:09:30Z"}],"visa":[],"liveness":{"score":80,"band":"hot","label":"Hiring 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