Finance Analytics Senior Data Scientist

Key Skills

Apache Kafka, Continuous Integration & Continuous Delivery - CI/CD, D3.js, Data Visualization, DevOps, Effective communication skills, ggplot, GLM Regression, Machine learning techniques, PowerBI, PyMC3, PySpark, Python Programming

Job Description

Salary: £70,000 - £90,000 (dependent upon experience) + bonus + benefits

Mars is leveraging data, insights, AI and technology to create the Finance of Tomorrow. In this future we will create advanced, forward-looking capabilities to drive and solve problems across the entire organization; from Inflation, Macro & Micro Economic factors, Cash Optimization, to Prime forecasting. The Senior Data Scientist will be at the forefront of this change, delivering business insights and developing predictive capabilities for Mars.

What you’ll do

Inspiring Mars associates to adopt data driven decision-making by developing advanced analytics methods using Machine Learning/AI. By mining vast amounts of data from company and external data sets for insights that will help solve business problems. Working within a multidisciplinary team alongside Big Data Engineering, Business Translation, Agile Delivery Scrum Masters, you’ll partner with Product Owners to solve business problems and drive insights that generate value.

Your day-to-day activities will include, but not be limited to, the following:

• Changing the way Finance does business through the establishment of innovative data science, data engineering, standards and methodologies: developing scalable solutions for diverse business segments; incorporate emerging technologies into the data analysis process; and influencing the scope and direction of projects
• Leveraging various ML algorithms and capabilities to provide innovative solutions that positively impact your stakeholder’s operations
• Analyzing data sets to determine the best end-to-end machine learning techniques to create solutions for solving complex business issues
• Drawing insights and presenting results in a cohesive, intuitive, and simplistic manner for both technical and non-technical end users
• Performing model training, validation/evaluation, persistence, and deployment of productionized machine learning models
• Integrating and mining large datasets from disparate sources to identify new insights and patterns that can predict the success of innovative solutions to business issues
• Conducting advanced statistical analyses of structured and unstructured datasets using a variety of modeling techniques, such as: linear regression, time-series, classification, neural network and decision tree models, CNN and RNN, gradient boosting and others; producing recommender system, customer segmentation & targeting, propensity modeling, churn modeling, lifetime value estimation, forecasting and others
• Coaching, leading and developing other data scientists across Finance Analytics team.
• Working with stakeholders of all levels to demonstrate the value and insights from advanced analytics

To do all this, you’ll need:

• Significant experience in an Applied Data Science role or equivalent, ideally within the CPG, Consumer Products, Retail or Financial Services industries
• Strong data conceptualization experience for the delivery of insightful, actionable analyses
• Solid knowledge of and experience with modelling techniques such as significance testing; GLM/Regression; Random Forest; Boosting; Trees; text mining and social network analysis
• Expert knowledge of Python and PySpark, nice to have experience using Kafka & creating Bayesian models with libraries like PyMC3.
• Knowledge and use of data visualization tools such as Plotly DASH, Power BI, D3, ggplot to improve decision making and productivity
• Experience validating and deployment of model and analytical techniques to determine trends and significant data relationships
• Experience using key external third party data sources including Nielsen/ IRI/ Storeviews, Kantar, Homescan Panel, Retail Execution, Shopper card, first party data and consumer surveys
• Extensive experience leveraging products within Azure (Data Factory, Databricks, App Service, Logic Apps etc.)
• Experience leveraging GIT for version control, preferably within Azure DevOps
• Experience creating and managing CI/CD pipelines to deploy code
• Knowledge of Infrastructure as code (IaC) concepts, preferably with experience in writing and deploying resources using IaC
• Experience using tools like Azure Resource Manager (ARM) Templates & Terraform
• Knowledge of and experience at writing unit tests that capture edge cases and running these unit tests within CI Pipelines
• Preferably, knowledge of how Active Directory within Azure can be used to manage resource permissions.
• Azure Key Vault to store secrets and use these secrets within DevOps, ADF & Databricks
• Experience setting up and running pyspark locally in order to perform local development
• Strong communication and presentation skills
• Bachelor’s degree in Analytics or related quantitative fields (Statistics, Operations Research, Mathematics, Econometrics etc.). An advanced degree is preferred

So if you’ve got the skills we need and you’re looking for the opportunity to really make a mark in a world-renowned and supportive business going through a period of fast and massive digital transformation, this could be the role you’ve been waiting for.