Usage of Conventional AI
Virtusa utilizes Artificial Intelligence (AI) technology for the purpose of conducting candidate pre-employment assessments. This AI may include both text-based and voice-based functionalities. The AI technology will have access to the data that you upload or provide during your interaction with the AI bot and will use such data solely for the purpose of performing assessment in accordance with Virtusa policies and practices. The AI is not used to substantially assist or replace discretionary employment decisions. Read our full employee privacy policy here.
Job description
Job OverviewJob SummaryThe Data Scientist will play a critical role in
designing, developing, and operationalizing advanced analytical and machine
learning solutions that drive measurable performance improvement across
Cubic Transportation Systems (CTS) programs.
This role focuses on applying data science techniques to large-scale
operational datasets—including device telemetry, transaction data, incident
data, call-center interactions, and ridership patterns—to generate
predictive insights, identify leading indicators, and support KPI/SLA
performance management.
Key ResponsibilitiesThe following are the key responsibilities of the
position. It is expected that most, if not all of these, are met by the
candidate:
Advanced Analytics & Data Science
Design and develop statistical and machine learning models to
support:Predictive maintenance and device reliability
Demand forecasting (e.g., ridership, call volumes, queue behavior)
Anomaly detection and out-of-band performance identification
KPI and SLA risk prediction and early-warning indicators
Apply appropriate techniques such as:Build and evaluate supervised and
unsupervised learning models.
Use ensemble methods (e.g., Random Forest, Gradient Boosting, XGBoost,
LightGBM) to improve model performance.
Perform feature engineering, model tuning, and validation using appropriate
metrics.
Develop and maintain end-to-end ML pipelines using libraries such as
scikit-learn, TensorFlow, or PyTorch.
Operational & Performance Analytics
Translate business and operational problems into analytical hypotheses and
model designs.
Support the development of leading indicators that complement lagging KPI
reporting.
Quantify drivers of performance variation across time, geography, device
type, and customer behaviour.
Produce clear, actionable insights that support operational decision-making
and performance improvement initiatives.
Model Deployment & Integration
Work with Data Engineers to productionize models using scalable pipelines
(e.g., Databricks, Azure Data Factory).
Ensure models are:Reproducible
Version-controlled
Monitored for performance drift
Key job details
About Virtusa
Teamwork, quality of life, professional and personal development: values that Virtusa is proud to embody. When you join us, you join a team of 30,000 people globally that cares about your growth — one that seeks to provide you with exciting projects, opportunities and work with state of the art technologies throughout your career with us.
Great minds, great potential: it all comes together at Virtusa. We value collaboration and the team environment of our company, and seek to provide great minds with a dynamic place to nurture new ideas and foster excellence.
Virtusa is an Equal Opportunity Employer. All applicants will receive fair and impartial treatment without regard to race, color, religion, sex, national origin, ancestry, age, legally protected physical or mental disability, protected veteran status, status in the U.S. uniformed services, sexual orientation, gender identity or expression, marital status, genetic information or on any other basis which is protected under applicable federal, state or local law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government-issued ID during each interview. All candidates must be authorized to work in the USA.
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