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About The Role
Role Purpose

As a Data Scientist the role is responsible for the delivery of machine learning algorithms, optimization solutions, complex quantitative analysis and a variety of custom data processes and tools. The individual will work closely with stake holders in the organisation to ensure that predictive analytics delivers high value across the business.

The key focus is to identify opportunities for predictive analytics and optimization across the business, present ideas, propose analytics / solution approach, gather, explore, cleanse, integrate and analyse data from large operational transactional datasets and external online sources, conduct statistical data exploration and quantitative analysis, assess data quality, share and verify
key findings with business, apply supervised and non-supervised machine learning algorithms, fine tune models, test and validate models, build optimisation engines, assess key model characteristics and overall impact, re-iterate certain phases of the process if necessary, assist to implement solution into production environment, educate business stakeholders on model output, impact and behaviour, assist / coach along the business process transformation lifecycle.

Reporting to The Head of Advanced Analytics, the role will be accountable to business functions whilst aligning and proactively working together with the rest of the Analytics, BI, Product, Trading and Marketing teams to support and enable them to meet their KPIs and new propositions.

Key Responsibilities

• Define, build and verify machine learning solutions, optimization engines and statistical models to maximize business value in Acquisition, Product management, CRM, Trading, Responsible Gambling, Finance and VIP management
• Use supervised machine learning approaches to exploit predictive value from large internal transactional datasets and external data sources
• Apply non-supervised machine learning approaches, advanced statistical and analytical techniques to extract general patterns and insights from data
• Understand how advanced analytic technique and application, such as decision trees and clustering techniques can help to solve business problems
• Enhance overall customer behavioural profile and improve organizational capability for optimized next best action
• Chose the best modelling approach for given business problem
• Identify target variable, define need for input data
• Conduct data preparation to integrate data from heterogeneous sources and prepare data ready for modelling / analysis
• Apply multivariate analysis to understand variables predictive power
• Use advanced data manipulation and creativity to invent new features that boost model performance
• Apply clustering algorithms to enhance customer behavioural segmentation
• Fine-tune model performance for best business benefit
• Compile an analytical plan for given business problem
• Define hypothesis/null hypotheses to be tested
• Monitor live model performance, apply statistical process control approaches to identify significant performance deviation and need for intervention / model refresh
• Develop algorithms that personalize content or automate actions at various customer touch points
• Strive to develop in-depth business domain knowledge to understand key drivers of business and fundamentals of customer behaviour
• Support business around key business optimization challenges such as bonus allocation and pricing
• Build custom solutions around real-time event streams and stream analytics
• Exploit opportunities around Big Data technologies and cloud processing
• Interact with business stakeholders to identify key opportunities for predictive analytics and optimization
• Gain agreement and support for analytical approaches and solutions amongst business stakeholders
• Articulate model characteristics and impact to business stakeholders efficiently, with less emphasis on technical details and more focus on commercial implications
• Educate business users and leaders on how to interpret and use model output
• Drive analytical projects through to delivery and embed them within the organisation, advocate predictive analytics to transform operational business processes
• Pilot and test new solutions, assess impact by using target / control concepts
• Monitor solution ROI over time
• Stakeholder management
• Work with BI developers to operationalize and fully automate any accepted piece of analysis
• Analytics across all digital brands (Coral, Ladbrokes, Gala Bingo and Casino)
• Be up to date with industry trends, embrace and adopt industry innovations on data technology and advanced analytics
• Identify potential new external data sources that can enhance data, assess value, build prototype data feeds / data collection processes and conduct proof of concept for data usability

Qualifications and Educational Requirements

• BSC in computer science, mathematics or statistics

Specialist Skills and Experience Required

• Excellent problem solving skills
• A true talent in working with data
• Experience of data analytics at a senior level
• Experience with modelling platforms R, Python, Knime, Statistica, SAS Miner or Stat, XLStat
• A balanced set of expertise across a number of domains including supervised and unsupervised machine learning techniques, data engineering, statistical analysis, custom tools development and business consulting.
• Strong understanding of the data mining methodology i.e. Crisp DM, SEMMA
• Experience in building models of decision trees, neural networks, random forests, logistic and linear regression, text mining, clustering, ensemble models, uplift modelling
• Methods for Validating Models: mean squared error, R squared, Confusion matrix, gains, ROC
• Expert data manipulation skills SQL, analytical functions, query optimization, Python automation
• Experience in Big Data environments (MS Azure, Spark, Hive) and/or stream analytics
• Ability to build relationships with key stakeholders to influence the best practice of data usage
• Good understanding of business processes within online gaming
• Passion for digital business
• Strong understanding and experience working with large transactional data sets, e.g. bet records, site visitor data, transformation and analytics conducted over billions of records
• Strong application of descriptive analytics and statistical techniques
• Strong presentation skills, able to simplify and articulate complex domain areas to senior stakeholders.
• A good knowledge of Sport and Interest in Sports betting
• Articulate and confident when presenting the output of your work
• Highly data and statistical driven rather than gut feel
• Capable of clarifying business hypothesis and apply analytical technique to test them
• Capability to interpret data and drive business conclusions
• Has or is keen to gain experience of building or rolling out advanced data mining models to business users
• Ability to work across functions and locations
• Good communicator