Data Scientist

ABOUT US

Wolters Kluweris a global leader in professional information services, providing software and content solutions for legal, business, tax, accounting, finance, audit, risk, compliance, and healthcare professionals. Serving clients in over 180 countries with operations in 40+, the company helps professionals make confident decisions and drive results. Headquartered in Alphen aan den Rijn, the Netherlands, it employs around 21,000 people worldwide.

The Global Finance Shared Services (FSS) is a finance function designed to bring together finance and accounting professionals to streamline and enhance processes and tools. Within FSS, the Finance Centre of Excellence (CoE) in Pune, India, provides centralized financial and analytical support across all Wolters Kluwer divisions. The team, comprising finance professionals and data scientists, focuses on finance reporting harmonization, technology enablement, and reporting and analytics enhancements.

In this role, you will be responsible for developing and maintaining data pipelines and ensuring data availability. Tools used will be mainly Snowflake and Informatica.

JOB QUALIFICATIONS

Education : Bachelor's degree in data science/analytics or Engineering in Computer Science, or related quantitative field.

Experience : At least 2-3 years of experience in the field of Data Analytics/Engineering.

Technical Skills :

• Experience with data pipeline tools (e.g., Apache Airflow, dbt, Azure Data Factory, Fabric, Informatica).
• Proficient in SQL and Python/PySpark for complex data transformations and automation
• Hands-on experience with cloud platforms (Fabric or AWS) and data warehouses (Snowflake or Synapse) for large-scale data integration and analysis
• Familiarity with Microsoft Fabric and its integration within modern data ecosystems
• Strong understanding of data modelling, schema design, and performance tuning
• Knowledge of CI/CD pipelines and version control systems like Git

Soft Skills :

• Strong analytical skills capable of multi-tasking in fast-paced, dynamic environment
• Strong written and verbal communication, including report writing and data storytelling
• Stay updated with industry trends and evolving tools Demonstrate a proactive approach to learning new techniques and technologies
• Work effectively across cross-functional teams

ESSENTIAL DUTIES

Model Development & Deployment:

• Develop predictive models to forecast key sales and marketing metrics solve to complex business problems and drive strategic insights.
• Lead the end-to-end lifecycle of data science projects, including data preparation, feature engineering, model development, validation, and deployment.

Data Analysis & Insights:

• Analyze large, complex datasets from multiple sources to identify trends, patterns, and opportunities that inform decision-making.
• Collaborate with finance and accounting teams to automate reconciliations, variance analysis, and error detection using advanced analytics and machine learning.

Innovation, Automation & Generative AI:

• Lead initiatives leveraging Generative AI (GenAI) technologies to automate the generation of financial reports, narratives, and data summaries, enhancing efficiency and accuracy.
• Explore and implement GenAI applications to improve natural language processing (NLP) capabilities for extracting insights from unstructured financial documents (e.g., contracts, invoices).
• Drive the adoption of GenAI-powered chatbots or virtual assistants to support finance shared services teams with data queries, process guidance, and routine task automation.

OTHER DUTIES

Support Data Infrastructure & Governance:

• Lead efforts to design, optimize, and scale data infrastructure in Microsoft Fabric, defining clear data requirements and overseeing robust ETL/ELT processes.
• Drive data governance initiatives by implementing data quality frameworks, ensuring thorough documentation, and standardizing practices across teams to maintain data integrity.

Enhance Reporting & Analytics Standards:

• Collaborate with analytics and BI teams to establish dashboard design standards, improve user experience, and ensure consistent KPI definitions across the organization.

Promote a Data-Driven Culture:

• Advocate for a data-driven culture, promoting the value of analytics in strategic and operational decision-making.
• Lead or support analytical training sessions or workshops for non-technical stakeholders to improve data literacy across the organization.

Cross-Functional Collaboration:

• Partner with business, product, and operational teams to provide expert data engineering support, enabling effective data-driven solutions and innovation across departments.

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