Job Description:
Role Overview:
We are seeking a highly skilled and collaborative Data Scientist to join the delivery team for a strategic Data & AI implementation project. The ideal candidate will support the platform’s advanced analytics use cases, including data exploration, statistical modeling, feature engineering, ML model development, and GenAI use case enablement. You will collaborate with data engineers, architects, BI developers, and business stakeholders to deliver actionable insights and intelligent solutions.
Key Responsibilities:
Data Exploration & Feature Engineering:
- Explore, profile, and analyze structured, semi-structured, and unstructured data across more than 11 enterprise source systems (e.g., SAP, Oracle Fusion, JIRA, Primavera, etc.).
- Perform data cleansing, feature selection, and transformation activities across Bronze and Silver layers of the Lambda Data Lakehouse architecture.
Advanced Analytics & ML:
- Develop, train, and optimize supervised and unsupervised machine learning models to support use cases such as customer churn prediction, financial chatbot insights, and operational efficiency improvements.
- Support integration and deployment of ML models into the data platform using tools like Google Cloud AI Platform, Python, Snowflake, and Power BI.
GenAI Enablement:
- Collaborate on use cases involving Generative AI and Large Language Models (LLMs) using structured and unstructured datasets.
- Work with data architects to ensure the Lakehouse architecture supports these models effectively.
Collaboration & Business Engagement:
- Participate in workshops and requirement-gathering sessions with customer’s business and technical teams.
- Translate business questions into data science problems, and present findings in actionable, business-friendly formats (dashboards, reports, presentations).
Quality, Testing & Governance:
- Document assumptions, modeling approaches, and validations.
- Participate in unit testing, QA, and UAT activities related to predictive analytics and AI models.
- Ensure compliance with NDMO, PDPL, and customer’s internal data governance policies.
Required Skills and Experience:
- Strong experience in data science or machine learning roles on large-scale enterprise data projects.
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Strong hands-on experience in:
- Python, Pandas, Scikit-learn, TensorFlow or PyTorch
- SQL (Snowflake or Google BigQuery preferred)
- Data wrangling and feature engineering
- Experience with ML lifecycle and MLOps tools on Google Cloud Platform (GCP).
- Familiarity with data versioning, CI/CD for ML, and monitoring ML pipelines.
- Knowledge of APIs, data integration methods, and working with virtualized layers (e.g., Denodo).
- Exposure to BI visualization tools like Power BI.
- Understanding of data privacy, security, and model fairness in regulated environments.
Preferred Qualifications:
- Google Cloud Certified Data Engineer / Machine Learning Engineer (preferred but not mandatory)
- Experience working in real estate or public sector is a plus.
- Understanding of data cataloging, metadata, and lineage concepts using Informatica Cloud.
Soft Skills:
- Strong analytical mindset and problem-solving skills.
- Excellent communication and stakeholder engagement abilities.
- Ability to work under pressure and deliver in fast-paced, multi-vendor environments
At DXC Technology, we believe strong connections and community are key to our success. Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing, productivity, individual work styles, and life circumstances. We’re committed to fostering an inclusive environment where everyone can thrive.
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