The Role

Work closely with global optimization solutions team to architect, develop, and maintain advanced reporting and data visualization capabilities on large volumes of data to support data insights and analytical needs across products, markets, and services
The candidate for this position will focus on Building solutions using Machine Learning and creating actionable insights to support product optimization and sales enablement.
Prototype new algorithms, experiment, evaluate and deliver actionable insights.
Drive the evolution of products with an impact focused on data science and engineering.
Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.
Perform data ingestion, aggregation, and processing on high volume and high dimensionality data to drive and enable data unification and produce relevant insights.
Continuously innovate and determine new approaches, tools, techniques & technologies to solve business problems and generate business insights & recommendations.
Apply knowledge of metrics, measurements, and benchmarking to complex and demanding solutions
Ensure that all AI solutions follow industry standards for data management and privacy, covering aspects like data collection, use, storage, access, retention, output, reporting, and quality at Mastercard.
Take a practical approach to AI, simplifying complex technical requirements to align with stakeholders needs.
Collaborate with global stakeholders to gather necessary information and define business problems.
Think creatively to link AI methodologies with real business challenges.
Identify common use case patterns to promote scalable AI through reusable models and a microservice approach.
Build AI/ML solutions using the latest advancements from industry and academia.
Use both open and proprietary technologies to address business problems.
Work effectively across teams and geographies, leveraging broader team expertise to achieve AI goals.
Partner with technical teams to deploy solutions in production environments.
Foster a culture of learning and continuous improvement in AI capabilities.

All About You

8+ years in Data Science, with a focus on AI strategy, execution, and solution development from the ground up.
A passion for AI, demonstrated through participation in competitions such as Kaggle.
Preferred experience or familiarity with cybersecurity, fraud, and risk solutions, including:
Deep learning algorithms, open-source tools, and statistical environments (Python, R, SQL).
Big data platforms like Hadoop, Hive, Spark, and GPU clusters for deep learning.
Classical machine learning techniques such as Logistic Regression, Decision Trees, K-Means, PCA, and Time Series models (ARIMA/ARMA).
Deep learning techniques including Random Forest, GBM, Neural Networks (CNNs, LSTMs), and optimization techniques (Adam, Adagrad, etc.).
Frameworks like TensorFlow, Keras, PyTorch, and XGBoost for production systems.
Experience or exposure to collaboration tools like Confluence, Bitbucket, ALM, etc.
Knowledge of the payments industry and experience with SAFe (Scaled Agile Framework) is a plus.

Effectiveness

Ability to manage assumptions and validate them with key stakeholders under tight deadlines, while keeping development on track.
Strong problem-solving skills, with the ability to break down complex issues and apply the right AI techniques to solve them.
Deep attention to detail, ensuring high confidence in developed solutions.
Strong understanding of technical system architecture and interdependencies, anticipating challenges and proactively solving them.

Core Capabilities

Excellent written and verbal communication skills.
Strong project management experience.
Background in Computer Science.

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