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11 months ago
Location: London
Job type: Permanent
Sector: Software Engineering
Category: Management Jobs
The Team

Many of our clients are on a ‘digital transformation journey’ to increase profits whilst reducing reputational, operational, financial and other risks. Our highly specialised Data Science and Engineering team in our KPMG ‘Lighthouse’ uses advanced analytical techniques and industrial scale technology platforms to help our clients accelerate their digital transformation journeys. Typical projects require extracting a variety of data at large scale, drawing deep insights using complex analytical algorithms, and visualising the results to articulate compelling and engaging stories that, in the end, deliver increased value from that data. Our UK team works closely together with data science and engineering teams around the world, supported by our global ‘Ignite platform’, in order to maximise our collective success

The Role
• At KPMG, our values define who we are and the way we do business. As a leading professional services firm, we know that our strength and capability come from our people – their different perspectives, experiences and backgrounds. From our inclusive leadership strategy to our diversity and inclusion targets – we’re making bold changes to who we are and what we do. Be part of it.
• Our Data Scientists support the whole firm on a wide variety of projects, across our Audit, Tax and Advisory business. We are experienced in managing diverse issues including business disruption, process optimisation, resource optimisation, fraud, regulatory compliance, dispute resolution, deriving value from contracts and much more
• Client related work: A Data Scientist would typically work under the guidance of a Senior Data Scientist and collaboratively with our business teams and our clients to show the art of the possible and to assess possible value and feasibility of applying data science in order to help solve specific business problems. This could include demoing to prospective clients, developing data strategies, leading feasibility studies, explorative data analysis, delivering minimal viable products or fully fledged projects including putting our models into production either on our own or our client’s environments.
• Asset development: Build data science assets (aka ‘accelerators’), in line with our UK and/or global strategy, to ensure we have the platforms and core assets in place to meet market demand. This could also include supporting our continuous improvement process around our own design and development processes e.g. about how we ensure the high quality that our clients require in an efficient manner.
• People: As a fast growing highly specialised team, several of team members will be involved in the running and growing of our team, e.g. through involvement in hiring and coaching colleagues, helping with knowledge management, organising team meetings or other events.

Roles & Responsibilities

• Operate as part of a team of Data Scientists on specific engagements, focused on the development, training and monitoring of data science solutions that accelerate our clients’ digital journeys.
• Work collaboratively with our business teams and our clients to develop solutions that solve business problems
• Support client engagements focused on large data sets and applying advanced analytical techniques, in diverse domains such as retail price optimisation, channel management, marketing strategies, customer intelligence, financial crime, risk management, healthcare digitisation, smart grids, etc.
• Monitor performance to ensure models perform as effectively as possible.
• Develop new, or tailor existing, analytical solutions designed for processing large data sets (e.g. using an Hadoop framework) and by applying advanced analytical techniques (e.g. machine learning, neural networks, NLP, A/B testing, etc.)
• Liaise with our advanced Data Engineers in terms of data engineering, model deployment and architecture activities to jointly build solutions that will interoperate seamlessly with other elements of the broader information architecture

The Person
• Well versed at applying advanced analytical techniques to large and varied data sets, generated and flowing at a rapid rate. Sample techniques include, but are not limited to:
o Applied machine learning
o Natural language processing
o Collaborative filtering and recommender systems
o Neural networks (including recurrent, convolutional)
o Event detection and tracking
o Graph Analytics

• Experience with:
o Analysing data growth and lead capacity/sizing activities to arrive at the most appropriate commercial and technical solution
o Generating and test working hypotheses, prepare and analyse historical data, identify patterns from samples for reporting of trends and support Predictive Analytics
o Leveraging data visualisation techniques and tools to effectively demonstrate patterns, outliers and exceptional conditions in the data
o Creating performance metrics and tracking processes to measure the effectiveness of Data Science solutions
o Deploying models into production, with awareness of the challenges.
o Conceptualising necessary data governance models to support the technical solution and assure the veracity of the data
o Operating within the exploratory and experimental aspects of Data Science, e.g. to tease out interesting and previously unknown insights from vast pools of data
o Working collaboratively with other members of the Data Science and Information Architecture teams to innovate and create compelling data-centric stories and experiences
o Proficient with programming languages used by data scientists and in big data platforms, like Python, R, Scala, Julia, Java.
• Track record in staying conversant in new analytic technologies, architectures and languages – where necessary – for storing, processing and manipulating this type of data
• Demonstrated Data Science consultancy skills, e.g. participate in hypotheses workshops, mentoring more junior team members, preparing reports and presenting data science results.
• Skilled to communicate with a variety of stakeholders in the organization
• Planning and organisation skills so as to work with a high performance team, handle demanding clients and multitask effectively

• Experience in data, data science, data engineering and/or other technology related capabilities
• BSc (ideally MSc) in Computer Science, Statistics, Engineering or similar technical field
• A combination of one or more of the following:
o Proficient with programming languages used by data scientists like Python, R, Scala, Julia, Java, C++
o Skills in data engineering technologies like Hadoop, HDFS, Spark, Elasticsearch
o SQL and NoSQL databases

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