The Role
Develops Next Best Offer models and advanced machine learning solutions using Python, PySpark, and R. Applies statistical modeling, forecasting, hypothesis testing, and classification techniques to generate actionable insights. Builds, validates, optimizes, and monitors scalable machine learning pipelines using KubeFlow and BentoML. Collaborates with cross-functional teams to translate business needs into data science solutions and improve customer engagement.
Summary Generated by Built In
Data Scientist
Job requirements
- Develop and implement Next Best Offer models and advanced data science solutions to drive business objectives and enhance customer engagement
- Apply statistical techniques such as hypothesis testing, t-tests, z-tests, and regression methods to extract actionable insights and support data-driven decision-making
- Build, validate, and optimize machine learning models using Python, PySpark, and R to ensure high accuracy and reliability
- Leverage probabilistic graph models and classification algorithms, including decision trees and support vector machines, to address complex business challenges
- Optimize and automate machine learning pipelines for scalable deployment using KubeFlow and BentoML, improving operational efficiency
- Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to inform business strategies
- Monitor model performance using evaluation metrics and recommend data-driven improvements to maintain model effectiveness
- Collaborate with cross-functional teams to translate business requirements into impactful data science solutions
- Python
- PySpark
- SAS
- SPSS
- R
- Probabilistic graph models
- Regression methods (linear and logistic)
- Forecasting methods (exponential smoothing, ARIMA, ARIMAX)
- TensorFlow
- PyTorch
- Scikit-learn
- CNTK
- Keras
- MXNet
- Decision trees
- Support Vector Machines (SVM)
- Distance metrics (Hamming, Euclidean, Manhattan)
- KubeFlow
- BentoML
- Experience with Great Expectations and Evidently AI for model validation and monitoring
- Expertise in deploying machine learning models in cloud-based environments
- Knowledge of advanced ensemble methods and boosting algorithms
- Familiarity with A/B testing and experimental design
- Background in recommendation systems and personalization algorithms
- Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a quantitative discipline relevant to data science
- Certification in Machine Learning or Data Science from a recognized institution such as Coursera, edX, or DataCamp
- Relevant certification in statistical analysis or analytics, such as SAS Certified Statistical Business Analyst
Experience Range: With 4 to 6 years of experience in advanced data science roles, including hands-on involvement in machine learning and statistical modeling projects Key Responsibilities:
Required Skills:
Preferred Skills:
Desired Qualifications:
Skills Required
- 4 to 6 years of experience in advanced data science, including hands-on machine learning and statistical modeling projects
- Proficiency in Python, PySpark, SAS, SPSS, and R
- Experience with probabilistic graph models, regression methods, forecasting methods, decision trees, support vector machines, and distance metrics
- Experience with TensorFlow, PyTorch, scikit-learn, CNTK, Keras, and MXNet
- Experience optimizing and automating machine learning pipelines using KubeFlow and BentoML
- Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a relevant quantitative discipline
- Machine Learning or Data Science certification from a recognized institution such as Coursera, edX, or DataCamp
- Relevant certification in statistical analysis or analytics, such as SAS Certified Statistical Business Analyst
- Experience with Great Expectations and Evidently AI for model validation and monitoring
- Expertise deploying machine learning models in cloud-based environments
- Knowledge of advanced ensemble methods and boosting algorithms
- Familiarity with A/B testing and experimental design
- Background in recommendation systems and personalization algorithms
Brillio Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Brillio and has not been reviewed or approved by Brillio.
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Healthcare Strength — Healthcare is considered comprehensive, including medical coverage for employees and dependents alongside life, disability, and accidental death protections. Feedback suggests these protections are a core strength of the package.
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Leave & Time Off Breadth — Time-off options include paid leave and parental leave, with flexible or ‘flexible PTO’ approaches cited in some contexts. Feedback suggests this breadth helps support work-life balance when team norms permit usage.
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Wellbeing & Lifestyle Benefits — Wellbeing offerings span counseling, financial-management sessions, fitness programs, and travel insurance, plus region-specific extras like discounted IT hardware and work-from-home essentials. Feedback suggests these add-ons enhance perceived value beyond core insurance.
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The Company
What We Do
Brillio is the leader in global digital business transformation, applying technology with a human touch. We help businesses define internal and external transformation objectives, and translate those objectives into actionable market strategies using proprietary technologies. With 2600+ experts and 13 offices worldwide, Brillio is the ideal partner for enterprises that want to quickly increase their core business productivity, and achieve a competitive edge, with the latest digital solutions.
