Data Scientist

• Become an expert point of contact and thought leader on your scope by mastering the details and principles pertaining to it.
• Some key areas we have to focus include: optimizing our Pricing capabilities, such as Dynamic / Surge Pricing, Discounts / Commissioning Optimization and Personalized Pricing, optimizing our Dispatching capabilities, optimizing our Fraud Detection / Revenue Leakage Prevention capabilities, optimizing our Recommendations / Personalization capabilities, amongst others.
• Identify and propose ML opportunities related to your scope and beyond
• Support with requirements definition and prioritization of ML projects
• Collaborate with cross-functional Engineering and Product teams
• Champion Data Quality as a critical input component to all ML solutions and Analytics
• Conduct Exploratory Data Analysis and subsequent Feature Selection / Engineering
• Utilize Statistical and ML techniques, as appropriate, for model training
• Validate and tune ML models
• Visualize analysis / modelling results focusing on meaningful, actionable metrics and insights
• Deploy and test ML applications using our MLOps infrastructureMonitor ML models on their technical performance, business impact and underlying data / concept drift
• Explain technical solutions in a business-friendly manner, supporting Product teams in their communications and reporting
• Maintain and improve technical documentation of delivered solutions
• Support the Data & AI domain in developing its technical capabilities, improving its ways of working and collaboration with other dependent teams
• Champion Data Culture, proactively share knowledge across teams, and participate and drive team building activities

Mid/Senior - Data Scientist

• We Are Hiring!
• Integrant is looking for game changers to join our team as "Data Scientist - AI & Machine Learning" with below roles and responsibilities:
• - Use mathematics, statistics, machine learning, and artificial intelligence techniques to extract knowledge and insights from structured, semi-structured, and unstructured data.
• - Design, develop, evaluate, and deploy predictive and prescriptive machine learning models.
• - Conduct open research and experimentation to develop innovative solutions for complex client challenges.
• - Engage with clients and stakeholders to understand business needs and translate them into AI and Data Science solutions.
• - Design and implement end-to-end Machine Learning and Generative AI solutions.
• - Build and optimize Retrieval-Augmented Generation (RAG) systems and intelligent agent-based applications.
• - Develop scalable model deployment and monitoring solutions using MLOps best practices.
• - Monitor model performance, detect concept drift, and continuously improve deployed systems.
• - Collaborate with software engineering teams to productionize AI applications and ensure reliability, scalability, and maintainability.
• - Mentor and coach junior Data Scientists and Machine Learning Engineers.
• - Lead technical discussions, knowledge transfer sessions, and client-facing AI engagements.
• - Stay current with emerging AI, Machine Learning, MLOps, and Generative AI technologies and frameworks.

Lead Data Scientist – AI, Machine Learning

• Use mathematics, statistics, machine learning, and artificial intelligence techniques to extract knowledge and insights from structured, semi-structured, and unstructured data.
• Design, develop, evaluate, and deploy predictive and prescriptive machine learning models.
• Conduct open research and experimentation to develop innovative solutions for complex client challenges.
• Engage with clients and stakeholders to understand business needs and translate them into AI and Data Science solutions.
• Design and implement end-to-end Machine Learning and Generative AI solutions.
• Build and optimize Retrieval-Augmented Generation (RAG) systems and intelligent agent-based applications.
• Develop scalable model deployment and monitoring solutions using MLOps best practices.
• Monitor model performance, detect concept drift, and continuously improve deployed systems.
• Collaborate with software engineering teams to productionize AI applications and ensure reliability, scalability, and maintainability.
• Mentor and coach junior Data Scientists and Machine Learning Engineers.
• Lead technical discussions, knowledge transfer sessions, and client-facing AI engagements.
• Stay current with emerging AI, Machine Learning, MLOps, and Generative AI technologies and frameworks.

Principal Data Scientist – AI & Machine Learning

• Multi-Domain Technical Strategy: Lead the development of ML solutions across diverse contexts, ensuring that models for Credit Risk, Pricing Elasticity, and Collection Optimization utilize shared infrastructure efficiently.
• MLOps Architecture: Champion the adoption of modern Model Serving frameworks and Feature Stores. Design workflows that ensure feature consistency between training and real-time inference.
• Engineering Standards: Establish rigorous standards for Data Versioning, experiment tracking, and Hyperparameter Optimization, ensuring all research is reproducible and production-ready.
• Production Deployment: Oversee the transition of models from notebook environments to low-latency production APIs. Ensure models are wrapped, containerized, and integrated seamlessly with backend services.
• Mentorship: Guide the team in best practices for Python software engineering, including testing strategies, code structure, and performance optimization.

Senior Data Scientist

• Build AI-powered systems using NLP, LLMs, and agentic workflows
• Develop NLP solutions for tasks such as text classification, entity extraction, intent detection, summarization, and sentiment analysis
• Design and implement agentic AI systems that reason over conversations, historical data, and business rules
• Apply traditional machine learning techniques for prediction, classification, and optimization
• Use operations research techniques (optimization, heuristics, simulations) to support logistics and operational decision-making
• Collaborate with product, engineering, operations, and commercial teams to translate business problems into data science solutions
• Build end-to-end pipelines: data ingestion, feature engineering, modeling, evaluation, deployment, and monitoring
• Deploy models and AI services to production using cloud platforms and modern MLOps practices

Associate Data Scientist

• Lead end-to-end data science projects from conception to deployment.
• Develop and implement advanced machine learning models and algorithms in banking applications.
• Conduct strategic data analysis to inform business decisions and identify new opportunities.
• Communicate complex technical findings to senior management and stakeholders.
• Drive innovation in data science methodologies and tools.

See all jobs on JobTailor

Mid/Senior - Data Scientist

Lead Data Scientist – AI, Machine Learning

Principal Data Scientist – AI & Machine Learning

Senior Data Scientist

Associate Data Scientist

Senior Data Scientist