Experience Range: 15 - 18 years of experience in advanced data science roles, with extensive leadership in designing and deploying statistical and machine learning solutions
Key Responsibilities:
1. Design and implement robust statistical models and machine learning algorithms for large-scale data analysis and predictive analytics
2. Lead end-to-end development of data science projects, including hypothesis testing, regression analysis, classification, and forecasting
3. Collaborate with cross-functional teams to define business requirements, translate them into analytical solutions, and drive measurable impact
4. Optimize and automate data pipelines using Python, PySpark, and R, ensuring efficient data processing and feature engineering
5. Develop, validate, and maintain probabilistic graph models and advanced statistical computing frameworks
6. Utilize industry-leading ML frameworks such as TensorFlow, PyTorch, and Sci-Kit Learn to build, train, and deploy models
7. Establish rigorous model evaluation and monitoring processes using tools like Great Expectations and Evidently AI
8. Mentor and guide junior data scientists, fostering technical excellence and continuous learning within the team
Required Skills:
1. Expertise in hypothesis testing, including T-Test and Z-Test methodologies
2. Advanced proficiency in regression techniques (linear and logistic)
3. Strong programming skills in Python, PySpark, and R/R Studio
4. Hands-on experience with SAS and SPSS for statistical analysis and computing
5. In-depth knowledge of probabilistic graph models
6. Experience with forecasting methods such as Exponential Smoothing, ARIMA, and ARIMAX
7. Practical use of classification algorithms including Decision Trees and Support Vector Machines (SVM)
8. Proficiency with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet
9. Familiarity with distance metrics (Hamming, Euclidean, Manhattan)
10. Working knowledge of Kubeflow and BentoML for model deployment and orchestration
Preferred Skills:
1. Experience implementing advanced model monitoring with Evidently AI
2. Expertise in data pipeline automation and orchestration using Kubeflow
3. Knowledge of emerging ML frameworks and architectures
4. Experience with large-scale distributed computing environments
5. Strong background in statistical validation and reproducibility best practices
Desired Qualifications:
1. Master’s or PhD degree in Data Science, Statistics, Computer Science, Mathematics, or a related field
2. Relevant certifications in machine learning, statistical analysis, or advanced data science
Know more about Data and AI: https://www.brillio.com/services-data-analytics/
Know what it’s like to work and grow at Brillio: https://www.brillio.com/join-us/
Equal Employment Opportunity Declaration
Brillio is an equal opportunity employer to all, regardless of age, ancestry, colour, disability (mental and physical), exercising the right to family care and medical leave, gender, gender expression, gender identity, genetic information, marital status, medical condition, military or veteran status, national origin, political affiliation, race, religious creed, sex (includes pregnancy, childbirth, breastfeeding, and related medical conditions), and sexual orientation.
#LI-AY1
Lead development of virtual pharmacology models by evaluating biological datasets, building reproducible data pipelines and machine learning proofs of concept, and preparing validated models for internal use and potential commercialisation.
Research, implement, and validate machine learning enhancements to unsecured underwriting models, improving predictive performance and credit decisioning while ensuring reliable, rigorous production deployment.
Own pricing data science from demand and elasticity modelling through forecasting and price optimisation, using rigorous experiments and governed economics to guide revenue and margin decisions.
As a Lead Data Scientist, you will drive revenue growth by developing and deploying machine learning models that enhance user engagement and optimize monetization strategies.
As a Staff Data Scientist, you'll lead data strategy and tackle complex, business-critical problems, influencing senior leaders and raising Data Science standards across Monzo.
