Data Scientist
Weekday AI Bengaluru, Karnataka, India
Technology, Information and Internet · 11-50 employees
About the role
Design, develop, and deploy scalable data science and machine learning solutions using Python and LLMs. Collaborate with cross-functional teams to translate business problems into actionable AI-driven insights and production-ready applications.
What they look for
Requirements
Requires 5+ years of professional experience in data science or AI with strong hands-on expertise in Python and Generative AI. Candidates should possess a solid understanding of machine learning algorithms, NLP, and experience with cloud-based AI/ML environments.
Full description
𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀
𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟯𝟱𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟱𝟱𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟯𝟱-𝟱𝟱 𝗟𝗣𝗔)
Experience: 5+ yrs
Location: Bengaluru
Job Type: Full-time
We are looking for an experienced Data Scientist with strong expertise in Python and Large Language Models (LLMs)to develop intelligent, data-driven solutions that address complex business and product challenges. The role combines advanced analytics, machine learning, generative AI, and software engineering to build scalable solutions that deliver measurable business value.
The ideal candidate will be comfortable working across the full data science lifecycle, from problem definition and data exploration to model development, experimentation, deployment, and performance monitoring.
Key Responsibilities
- Design, develop, and deploy data science and machine learning solutions using Python.
- Analyse large and complex datasets to identify meaningful patterns, trends, and actionable insights.
- Develop predictive models, statistical models, classification, regression, clustering, and other machine learning solutions.
- Work extensively with Large Language Models (LLMs) to develop generative AI and NLP-based applications.
- Build solutions involving prompt engineering, embeddings, retrieval-augmented generation (RAG), text classification, summarisation, and information extraction.
- Evaluate and compare LLMs and machine learning approaches based on accuracy, relevance, latency, scalability, and cost.
- Develop data pipelines and preprocessing workflows for structured and unstructured data.
- Integrate AI/ML models and LLM capabilities into production applications through APIs and services.
- Conduct experiments, hypothesis testing, model evaluation, and performance optimisation.
- Collaborate with software engineers, product managers, analysts, and business stakeholders to understand requirements and define solutions.
- Translate business problems into measurable data science and machine learning objectives.
- Build prototypes and proof-of-concepts and take successful solutions toward production.
- Monitor model performance and identify opportunities for continuous improvement.
- Maintain clean, reusable, well-tested, and production-ready Python code.
- Document methodologies, experiments, models, and technical decisions.
- Stay current with developments in generative AI, LLMs, machine learning, NLP, and emerging data science technologies.
What Makes You a Great Fit
- 5+ years of professional experience in data science, machine learning, AI, or a closely related field.
- Strong hands-on expertise in Python for data science, machine learning, automation, and application development.
- Strong practical experience working with LLMs and Generative AI.
- Good understanding of machine learning algorithms, statistical modelling, feature engineering, and model evaluation.
- Experience with NLP and unstructured text data.
- Strong understanding of LLM concepts including prompt engineering, embeddings, vector search, RAG, and model evaluation.
- Experience with machine learning and data science libraries such as Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.
- Experience working with SQL and relational or NoSQL databases.
- Ability to build scalable data pipelines and integrate models into production systems.
- Strong analytical, problem-solving, and experimentation skills.
- Experience with cloud-based AI/ML environments such as AWS, Azure, or GCP is an advantage.
- Familiarity with MLOps, model deployment, APIs, Docker, or CI/CD practices is desirable.
- Excellent communication skills with the ability to explain technical findings and AI concepts clearly.
- Strong ownership mindset and ability to independently drive data science initiatives from experimentation through delivery.
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related discipline is preferred.
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