Job Description
We’re looking for a sharp, curious, and driven Associate Data Scientist – someone who doesn’t just crunch numbers but genuinely obsesses overturning data into impact. If you’re passionate about Machine Learning, Generative AI, and Advanced Analytics and want to apply them to real, complex business problems – this is your launchpad.
You’ll work alongside seasoned Data Scientists, Data Engineers, and Software Engineers – collaborating directly with business stakeholders across verticals to build predictive models, generate actionable insights, and ship AI/ML solutions that operate on a scale. From forecasting and optimization to automation and GenAI applications – you’ll get hands-on exposure to the full spectrum of modern Data Science.
Primary Responsibilities:
- Analyze & Discover – explore structured and unstructured datasets through deep EDA, feature engineering, and data validation to uncover patterns that drive business decisions
- Build & Optimize – develop, evaluate, and fine-tune machine learning and statistical models that solve real problems
- Communicate & Influence – create dashboards, reports, and visualizations that translate complex findings into clear, actionable narratives for stakeholders
- Collaborate & Ship – work hand-in-hand with Data Engineers to build reliable data pipelines and deploy AI/ML solutions into production
- Automate & Improve – identify and drive process improvement and automation opportunities across business functions
- Document & Share – maintain clear documentation of methodologies, experiments, and technical solutions
- Learn & Evolve – stay on the cutting edge of AI, ML, Generative AI, and analytics – and bring those ideas back to the team
- Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so.
Must have Skills:
- Core Data Science – strong foundation in statistics, probability, ML/DL algorithms, and model evaluation techniques
- Python & SQL – solid proficiency; you think in Python and speak fluent SQL
- ML Toolkit – hands-on experience with Pandas, NumPy, Scikit-learn, and at least one deep learning framework (PyTorch / TensorFlow)
- Generative AI & LLMs – working knowledge of large language models (GPT, LLaMA, etc.), prompt engineering, and prompt optimization techniques
- RAG & Embeddings – understanding of Retrieval-Augmented Generation architectures, vector databases (FAISS, Pinecone, Weaviate), and embedding strategies
- LLM Frameworks – exposure to LangChain, LangGraph, or similar orchestration frameworks
- Communication – ability to distill complex analysis into clear insights for both technical and non-technical audiences
- Mindset – strong problem-solving instincts, intellectual curiosity, and a bias for action.
Good to have skills :
- AI Agents & Autonomous Systems – experience building AI agents, chatbots, or agentic workflows that operate with minimal human intervention
- Multimodal AI – exposure to models spanning text, image, audio, or video (e.g., vision-language models, speech-to-text)
- Model Fine-Tuning – hands-on experience with LLM fine-tuning (LoRA, QLoRA, PEFT) and working with LLM APIs at scale
- MLOps / LLMOps – familiarity with model deployment pipelines, CI/CD for ML, experiment tracking (MLflow, Weights & Biases), and API serving
- Cloud & Containers – working knowledge of AWS (SageMaker, S3, Lambda) and Docker; bonus for Kubernetes exposure
- Real-World Problem Solving – experience with applied use cases like forecasting, optimization, automation, or recommendation systems
- Responsible AI – awareness of AI safety, bias mitigation, hallucination risks, and model governance practices
- Agile & Cross-Functional Collaboration – experience thriving in fast-paced, cross-functional team environments with iterative delivery cycles
- Documentation & Storytelling – ability to maintain clean technical documentation and present findings that drive decisions.
Required Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field
- 1+ years of experience in Data Science, Machine Learning, Analytics, or related domains
- Experience with data analysis and machine learning libraries such as Pandas, NumPy, and Scikit-learn
- Understanding of supervised and unsupervised learning techniques
- Familiarity with data visualization tools such as Tableau, Matplotlib, or Seaborn
- Solid foundation in statistics, probability, and machine learning concepts
- Proficiency in Python and SQL
- Proven solid analytical, problem-solving, and communication skills
- Proven ability to work collaboratively in a cross-functional team environment
Preferred Qualification:
- Basic understanding of cloud platforms, MLOps, or model deployment concepts
