Senior Data Scientist
Quantiphi
full-time
Posted on:
Location Type: Remote
Location: United States
Visit company websiteExplore more
Job Level
About the role
- Lead end-to-end data science initiatives for predictive analytics use cases such as demand forecasting, churn prediction, and risk modeling
- Translate business requirements into ML problem statements and define appropriate modeling approaches
- Design, build, and deploy machine learning models using traditional ML techniques (regression, classification, clustering, time series)
- Drive feature engineering, data preparation, and exploratory data analysis to improve model performance
- Develop and manage scalable ML pipelines from data ingestion to model deployment
- Deploy and manage models on AWS using services such as SageMaker
- Ensure model performance through validation, monitoring, and periodic retraining
- Collaborate with data engineering and MLOps teams to productionize ML solutions
- Apply best practices for model governance, explainability, and responsible AI
- Mentor junior data scientists and provide technical leadership while remaining hands-on
- Communicate insights, model outputs, and recommendations effectively to business stakeholders
Requirements
- 8+ years of relevant hands-on technical experience implementing and developing cloud solutions on AWS
- Strong experience leading predictive analytics initiatives using traditional ML techniques including regression, classification, clustering, and time series forecasting
- Hands-on experience with time series forecasting models including SARIMA, Prophet, and other ML-based forecasting approaches
- Proficiency in Python with experience in libraries such as scikit-learn, XGBoost, Pandas, NumPy
- Knowledge of a variety of machine learning techniques (Supervised/unsupervised etc.) (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks
- Proven ability to translate complex business problems into scalable ML solutions, driving feature engineering strategies and end-to-end model development
- Hands-on experience on AWS Machine Learning services
- Proven experience using AWS Sagemaker leveraging different types of data sources, Training jobs, real-time and batch Inference, and Processing Jobs
- Experience leading model deployment on AWS SageMaker with a strong focus on performance optimization, model governance, and measurable business impact
- Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.
- Ability to create end to end solution architecture for model training, deployment and retraining using native AWS services such as Sagemaker, Lambda functions, etc.
- Experience in building model monitoring and explainability workflows in production environments
Benefits
- Work where innovation happens
- Upskill and discover your potential
- Collaborate with a diverse and talented set of professionals
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningpredictive analyticsregressionclassificationclusteringtime series forecastingfeature engineeringdata preparationmodel validationmodel retraining
Soft Skills
technical leadershipmentoringcommunicationcollaborationproblem-solving