Data Scientist-ii

Our Purpose
- Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build _a sustainable economy_ where everyone can prosper. We support a wide range of digital payments choices, making _transactions secure,_ simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential._

Title and Summary

Data Scientist-II

Role Overview

Key Responsibilities
- Data Science and Advanced Analytics
- Identify and understand customer challenges, working cross-functionally to translate them

into business opportunities focused on AI solutions.
- Develop, implement, and deploy AI solutions, including GenAI models, to solve complex

business problems and enhance operational efficiency.
- Perform exploratory data analysis to understand data structure and characteristics, using

advanced statistical methods and machine learning algorithms.
- Analyze and interpret model results to generate actionable insights and recommendations

for stakeholders.
- Conduct market and trend scouting to stay current with the latest tools, techniques, and

trends in AI and data science.
- Data Engineering and Pipeline Development (Preferred, a big PLUS)
- Architect, build, and maintain scalable data pipelines and ETL processes, ensuring high
- quality data for analytical and machine learning models.

ETL tools, data warehouses, and big data technologies (e.g., Hadoop, Spark).
- Collaborate with engineering teams to define data requirements, build robust data models,

and optimize pipeline performance.
- Collaboration and Integration
- Work closely with cross-functional teams to integrate AI solutions into business processes

and workflows, driving measurable impact.
- Interact with stakeholders at various levels, effectively presenting findings through

simplified visual displays of complex quantitative information.
- Act as an advocate for AI and data-driven solutions, fostering a culture of continuous

learning and development.
- Deployment and Operationalization
- Use tools like ADO or Jenkins for CI/CD to streamline model deployment and monitor model

performance in production.
- Perform statistical evaluation of models, tuning, and validation for optimization, neural

networks, and machine learning workflows.

Basic Qualifications
- Educational Background: Bachelors/Master’s in Computer Science, Data Science, Statistics,

Engineering, or a related field. (B tech in CS highly preferred)
- Experience:

- 5+ years in data science or data engineering projects, with a strong foundation in

statistics, machine learning algorithms, and end-to-end ML component development.
- Proven expertise in working on engagements from use-case conceptualization to design,

build, train, and deploy ML models.
- 6+ years working with Big Data technologies (e.g., Hadoop, Spark) and cloud platforms

(AWS, Azure, Google Cloud).
- Programming and Development:

- Advanced proficiency in Python and R for data science and machine learning
- Experience with data science libraries such as NumPy, pandas, scikit-learn, PyTorch,

TensorFlow, Keras, and spaCy.
- Familiarity with additional languages like SQL, Scala, and Java for data pipeline

integration.
- Data Engineering and Pipeline Tools:

- Strong experience with ETL tools (e.g., Apache NiFi, Talend, Informatica) and orchestration

frameworks (e.g., Apache Airflow, Luigi).
- Hands-on experience with big data processing frameworks like Apache Spark, Hadoop, Kafka

for data streaming, and batch processing.
- Proficiency with data warehousing and storage solutions (e.g., Snowflake, BigQuery, Amazon

Redshift, Azure Synapse).
- Machine Learning and Deep Learning:

- Solid understanding of statistical and machine learning algorithms (e.g., regression,

classification, clustering, decision trees, neural networks).
- Proficient in deep learning frameworks such as TensorFlow, PyTorch, Keras, and specialized

NLP libraries (e.g., Hugging Face Transformers, BERT, GPT).
- Familiarity with model deployment frameworks, including TensorFlow Serving, ONNX, and

MLflow for model tracking and versioning.
- Data Visualization and Reporting:

- Expertise with data visualization tools like Tableau, Power BI, Plotly, and Seaborn for creating

dashboards and visual insights.
- Proficiency in Matplotlib, ggplot2 (R), or other visualization libraries for in-depth analysis and

presentation.
- Data Storage and Management:

- Strong understanding of SQL, NoSQL (e.g., MongoDB, Cassandra), and data lake solutions

(e.g., AWS S3, Azure Data Lake, Google Cloud Storage).
- Experience in database optimization, indexing, and query tuning for RDBMS such as

PostgreSQL, MySQL, and Oracle.
- Cloud Platforms and Infrastructure:

- Proficient in cloud services, including AWS (SageMaker, Redshift, Lambda), GCP (BigQuery,