Note: The job is a remote job and is open to candidates in USA. VBeyond Corporation is seeking a Senior Data Scientist to lead predictive analytics and machine learning initiatives across enterprise use cases. The role owns the full ML lifecycle, including problem framing, model development, AWS deployment, monitoring, governance, and mentoring junior data scientists.
Responsibilities
- Drive end-to-end data science initiatives for high-impact use cases including demand forecasting, churn prediction, and risk modeling
- Translate complex business questions into well-defined ML problems and select the right modeling approach for each
- Design, build, and deploy machine learning models using traditional techniques—regression, classification, clustering, and time series
- Lead feature engineering and exploratory analysis to continuously elevate model performance
- Develop and manage scalable ML pipelines —from data ingestion to deployment and monitoring
- Deploy and maintain models on AWS (especially SageMaker), ensuring robustness, low latency, and cost-efficiency
- Monitor model health through validation, performance tracking, and automated retraining cycles
- Collaborate closely with data engineering and MLOps teams to operationalize ML solutions
- Champion responsible AI by implementing model governance, explainability, and fairness best practices
- Mentor and guide junior data scientists while staying hands-on with code and architecture
- Communicate insights and recommendations clearly to both technical and non-technical stakeholders
Skills
- • 8+ years of hands-on experience developing and deploying cloud-based ML solutions on AWS
- • Proven track record leading predictive analytics projects using regression, classification, clustering, and time series forecasting
- • Deep expertise in time series modeling (SARIMA, Prophet, and ML-based forecasting approaches)
- • Advanced Python skills with libraries like scikit-learn, XGBoost, Pandas, and NumPy
- • Broad knowledge of ML techniques (supervised, unsupervised, decision trees, neural networks, etc.) and their real-world trade-offs
- • Demonstrated ability to translate business needs into scalable ML solutions , driving feature strategy and end-to-end development
- • Extensive experience with AWS SageMaker —training jobs, real-time/batch inference, processing jobs, and diverse data sources
- • Strong background in model deployment on SageMaker with a focus on performance, governance, and business impact
- • Solid understanding of MLOps —model lifecycle management, CI/CD for ML, and production architecture best practices
- • Proficiency with at least one workflow orchestration tool (Airflow, Step Functions, SageMaker Pipelines, Kubeflow, etc.)
- • Ability to design end-to-end solution architectures on AWS (SageMaker, Lambda, and related services)
- • Hands-on experience building model monitoring and explainability workflows in production
- • Experience defining and driving model governance frameworks and performance monitoring strategies at scale
- • Strong cross-functional collaboration skills—working with developers, QA, project managers, and business stakeholders
- • Familiarity with Generative AI development
- • Experience with Infrastructure as Code (IaC) and CI/CD pipelines
Benefits
- Remote (US)
Company Overview