Senior Data Scientist

**Company Description**
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

**Qualifications**
- ** 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.

**Additional Information**
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.