.Explore our latest thought leadership, ideas, and insights on the issues that are shaping the future of business and society.Choose a partner with intimate knowledge of your industry and first-hand experience of defining its future.Discover our portfolio – constantly evolving to keep pace with the ever-changing needs of our clients.Become part of a diverse collective of free-thinkers, entrepreneurs and experts – and help us to make a difference.See our latest news, and stories from across the business, and explore our archives.We are a global leader in partnering with companies to transform and manage their business by harnessing the power of technology.Key Responsibilities:Analyze large datasets to identify trends, patterns, and relationships that can drive business insights.Perform exploratory data analysis (EDA) to gain a deeper understanding of datasets and prepare them for modeling.Develop machine learning models for classification, regression, clustering, and recommendation systems using Python libraries (e.G., Scikit-learn, TensorFlow, Keras).Fine-tune and optimize models for better performance, accuracy, and scalability.Clean and preprocess raw data from various sources to ensure quality and readiness for analysis and model development.Handle missing data, outliers, and feature engineering to enhance model performance.Apply statistical methods such as hypothesis testing, A/B testing, and time series analysis to draw insights from data.Conduct experiments to validate assumptions and hypotheses for business applications.Create data visualizations using tools like Matplotlib, Seaborn, or Plotly to convey insights clearly to stakeholders.Present findings and actionable insights to business leaders and non-technical teams in a clear and concise manner.Work closely with cross-functional teams (engineering, business, marketing) to understand data requirements and deliver solutions.Collaborate on the development of new data products and features that add value to the business.Stay current with the latest trends in data science, machine learning, and artificial intelligence.Explore new tools, techniques, and methodologies to improve analysis and modeling processes.What You'll Bring:Strong proficiency in Python, with expertise in libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow, Keras, PyTorch, etc.Experience with scripting and automation for data analysis and model deployment.Hands-on experience with machine learning algorithms such as linear regression, logistic regression, decision trees, SVMs, k-means clustering, random forests, and gradient boosting.Knowledge of deep learning techniques, especially for neural networks and natural language processing (NLP).Solid foundation in statistics, including hypothesis testing, statistical inference, and experimental design.Experience with A/B testing, time series analysis, and Bayesian statistics