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Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change.
By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses.
From prototype to real-world impact - be part of a global shift by doing work that matters.
Job Description
Role Overview
The Lead Data Scientist is responsible for developing and deploying advanced AI/ML models, leveraging statistical techniques, machine learning, and deep learning to extract actionable insights. This role requires strong expertise in Python-based AI/ML development, big data processing, and cloud-based AI platforms (Databricks, Azure ML, AWS SageMaker, GCP Vertex AI).
Key Responsibilities
- Data Exploration & Feature Engineering
- Perform thorough Exploratory Data Analysis (EDA) and identify key variables, patterns, and anomalies.
- Engineer and select features for optimal model performance, leveraging domain understanding.
- Machine Learning & Statistical Modelling
- Implement both classical ML methods (regression, clustering, time-series forecasting) and advanced algorithms (XGBoost, LightGBM).
- Address computer vision, NLP, and generative tasks using PyTorch, TensorFlow, or Transformer-based models.
- Model Deployment & MLOps
- Integrate CI/CD pipelines for ML models using platforms like MLflow, Kubeflow, or SageMaker Pipelines.
- Monitor model performance over time and manage retraining to mitigate drift.
- Business Insights & Decision Support
- Communicate analytical findings to key stakeholders in clear, actionable terms.
- Provide data-driven guidance to inform product strategies and business initiatives.
- Ethical AI & Governance
- Ensure compliance with regulations (GDPR) and implement bias mitigation.
- Employ model explainability methods (SHAP, LIME) and adopt best practices for responsible AI
- Technical Skills
- Programming:
Python (NumPy, Pandas), R, SQL. - ML/DL Frameworks:
Scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers. - Big Data & Cloud:
Databricks, Azure ML, AWS SageMaker, GCP Vertex AI. - Automation:
MLflow, Kubeflow, Weights & Biases for experiment tracking and deployment. - Architectural Competencies
- Awareness of data pipelines, infrastructure scaling, and cloud-native AI architectures.
- Alignment of ML solutions with overall data governance and security frameworks.
- Soft Skills
- Critical Thinking:
Identifies business value in AI/ML opportunities. - Communication:
Distils complex AI concepts into stakeholder-friendly insights. - Leadership:
Mentors junior team members and drives innovation in AI.
Discover some of the global benefits that empower our people to become the best version of themselves:
- Finance:
Competitive salary package, share plan, company performance bonuses, value-based recognition awards, referral bonus; - Career Development:
Career coaching, global career opportunities, non-linear career paths, internal development programmes for management and technical leadership; - Learning Opportunities:
Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass-it-on sessions, workshops, conferences; - Work-Life Balance:
Hybrid work and flexible working hours, employee assistance programme; - Health:
Global internal wellbeing programme, access to wellbeing apps; - Community:
Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations.
Key Skills
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