Senior Analyst, Data Strategy

Mastercard’s interview process typically starts with a timed online assessment covering quantitative aptitude and a coding problem (Python/SQL). Successful candidates move to a technical interview where they discuss machine‑learning projects, data‑quality scenarios and may be given a live coding exercise. The final HR round focuses on cultural fit, motivation and career aspirations. Preparation should include strong fundamentals in ML, data pipelines and practicing coding problems under time pressure.

Mastercard promotes an inclusive, innovation‑driven culture where employees are encouraged to experiment and upskill. The company offers structured learning programs, mentorship and a healthy work‑life balance, with flexible work‑from‑home options for many roles.

Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, Engineering or related field. Minimum 60% aggregate (or CGPA 6.0/10) in the qualifying degree. No backlogs at the time of joining. Freshers and candidates from the 2025‑2027 graduating batches are encouraged to apply.

Round 1: Online assessment (aptitude + coding) → Round 2: Technical interview (ML concepts, data‑quality case studies, coding) → Round 3: HR interview (fit, motivation, cultural alignment)

Mastercard powers economies and empowers people in more than 200 countries and territories. As a global leader in digital payments, the company combines cutting‑edge technology, extensive networks and strategic partnerships to deliver secure, simple and innovative payment solutions. Mastercard’s culture is built around inclusion, continuous learning and a strong sense of purpose – helping customers and communities thrive in a sustainable economy. The Enterprise Data Quality (EDQ) team within the AI & Data Office is tasked with creating a world‑class data quality program that ensures Mastercard’s data assets are accurate, complete and fit‑for‑purpose. The Senior Analyst, Data Strategy will be a key player in this ecosystem, driving the acquisition, integration and remediation of third‑party data sources. The role sits at the intersection of data engineering, machine learning and business strategy, collaborating with data quality, compliance, product and engineering teams to turn raw data into reliable, actionable insights. **Key responsibilities** 1. Design, build and deploy AI/ML solutions that automate data‑quality checks, forecasting, optimization and remediation across cloud and on‑prem environments. 2. Lead end‑to‑end development of machine‑learning models for use cases such as anomaly detection, NLP‑based data classification and generative‑AI data enrichment. 3. Perform exploratory data analysis and feature engineering using Python (NumPy, Pandas, Scikit‑learn) and SQL on large‑scale datasets stored in Snowflake, Azure Data Lake or AWS S3. 4. Implement MLOps pipelines with CI/CD tools (GitHub Actions, Azure DevOps) and model‑monitoring frameworks (MLflow, Azure Monitor) to ensure production reliability. 5. Apply data‑governance standards, privacy regulations and metadata management practices throughout the data‑lifecycle. 6. Partner with business SMEs to translate requirements into technical specifications and validate data suitability for new product initiatives. 7. Automate workflow orchestration using Airflow or Azure Data Factory and monitor pipeline performance. 8. Create documentation, training material and knowledge‑transfer sessions for cross‑functional stakeholders. 9. Contribute to modernization initiatives by recommending improvements to data pipelines, security controls and analytics platforms. 10. Stay abreast of emerging AI technologies (LLMs, vector databases, generative agents) and evaluate their applicability to Mastercard’s data strategy. **Tech stack**: Python (NumPy, Pandas, Scikit‑learn, PyTorch/TensorFlow), SQL, Azure/AWS/GCP, Databricks, Snowflake, Azure OpenAI, MLOps tools (MLflow, Docker, Kubernetes), Airflow, Azure Data Factory, CI/CD pipelines. **Growth path**: Successful performers can progress to Lead Analyst → Manager – Data Strategy → Senior Manager → Director – AI & Data, with opportunities to lead enterprise‑wide data‑governance programs and influence product roadmaps. **Why join Mastercard?**: You will work for a globally recognized brand that invests heavily in AI, cloud and data innovation. The role offers exposure to massive, real‑world datasets, cross‑functional collaboration, and a clear career ladder. Mastercard’s commitment to diversity, sustainability and continuous learning makes it an ideal place for ambitious data professionals to grow.