Petur Einarsson
About
Petur Einarsson is from London, England, United Kingdom. Petur is currently AI Specialist at BJSS, located in London Area, United Kingdom. In Petur's previous role as a Señor Data Scientist at BJSS, Petur worked in London, England, United Kingdom until Dec 2023. Prior to joining BJSS, Petur was a Data Scientist at BJSS and held the position of Data Scientist at London, United Kingdom. Prior to that, Petur was a Data Scientist at The Very Group, based in London, United Kingdom from Sep 2017 to Jan 2019. Petur started working as Masters Student (MSc), Big Data Science at Queen Mary University of London in London in Sep 2016. From Jan 2016 to Jun 2016, Petur was Accountant & Financial Analyst at Nexeo Solutions, LLC, based in Barcelona Area, Spain. Prior to that, Petur was a Interim Accounts Payable Team Lead at Nexeo Solutions, LLC, based in Barcelona Area, Spain from May 2015 to Dec 2015. Petur started working as Accounts Payable Specialist at Nexeo Solutions, LLC in Barcelona Area, Spain in Aug 2014.
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Petur Einarsson's current jobs
Petur Einarsson's past jobs
Computer Vision, Healthcare IVF: https://www.bjss.com/case-studies/care-fertility-transforms-ivf-selection-with-ai ➤ Lead a team of Data Scientists to develop a Computer Vision Algorithm to predict the likelihood of a fertilised embryo leading to a successful pregnancy from time-lapse images of the embryo developing in a lab ➤ Developed and tested multiple Computer Vision models using Keras/Tensorflow ➤ Built image processing pipelines using Dask and OpenCV to handle over 1TB of image data, using Azure Machine Learning (AzureML) ➤ Lead client engagement and project planning ➤ Successfully pitched new opportunities for Computer Vision to assist with manual labelling of images, leading to ongoing work Multiple roles, Retail & Commercial Banking: ➤ Project lead for a mixed team of Data Scientists, Data Analysts and Data Engineers across 3 workstreams; New Product Customer Insights, Home Insurance demand forecasting & Graph Analysis of customer needs. ➤ Lead Data Scientist, responsible for developing classification and regression machine learning algorithms for different 4 banking products. Deployed models as part of a larger Next Best Action (NBA) programme. Computer Vision, Consumer Goods: ➤ Created Proof of Concept (PoC) to detect potential faults in a factory using CCTV video feeds ➤ Lead a team of Data Scientists to expand the PoC, identify and apply it to different use-cases ➤ Deployed algorithms onto a live feed on a Stack Edge GPU enabled device using Docker Internal Roles, BJSS ➤ Squad Lead (People Manager) of 8 people at a time. Responsible for career development, well-being and performance reviews ➤ Performing technical interviews for Data Scientists
Computer Vision, Airport: ➤ Created Proof-of-Concept (PoC) Computer Vision model to detect Illegal Wildlife Trafficking for a major UK airport in cooperation with Microsoft ➤ Expanded on the original PoC model with different use cases and helped deploy the model onto a GPU-enabled Azure Stack Edge device Analysis, Healthcare: ➤ Web scraping semi-structured data and developed a PowerBI dashboard to analyse results for a COVID risk assessment tool Forecasting, Insurance: ➤ Built a Cash reserve time-series forecasting model (Facebook Prophet) to forecast Cash Reserves. The model ran in seconds and halved the error rate, compared to the BAU process which took weeks to do manually in Excel. Forecasting, Construction: ➤ Built a Cashflow forecasting model (Facebook Prophet) to replace an Excel-based forecasting calculation for a small engineering company NLP, Medical: ➤ Tested, improved and refactored an existing Natural Language Processing (NLP) algorithm that classified medical documents ➤ Created a project plan for the next iterations of the project Consulting, Aerospace: ➤ Conducted interviews and workshops to build an 'as-is' state of their MLOPs capability ➤ Generated a comprehensive report to outline areas for improvement Consulting, Insurance: ➤ Conducted interviews and workshops to build an 'as-is' state of their MLOPs capability ➤ Proposed steps for improving ways of working and steps on moving a Machine Learning model from experimentation into production Consulting, Energy: ➤ Conducted interviews and workshops to create a project plan for a pricing optimisation platform Consulting, Pensions: ➤ Conducted interviews and workshops to identify problems and bottlenecks in existing data migration processes ➤ Evaluated internal tools and outlined ETL tools that could help solve issues ➤ Created PoC apps to demonstrate the ability for rapid data profiling and mapping data between systems ➤ Proposed project plan for process improvements
(Very.co.uk / Littlewoods) A part of an Agile team supporting Marketing. A cross-site, cross-functional team of Data Scientists, Data Engineers, Analysts and Product Owners. Liaised with senior stakeholders, proposed possible solutions and estimated scope. Implemented Data Science and Data Engineering solutions to solve problems - Improved speed and accuracy of the company's AI-powered targeted marketing algorithm. Created a dashboard to analyse its results; Later adopted for all campaigns. - Improved an existing Customer Present Value metric and developed models for Future Value. - Interviewed potential candidates and evaluated coding challenges. - Delivered internal training sessions, hosted an on-site meetup and represented the company at a career’s fair.
Prepare journal entries and monthly, quarterly and yearly financial reporting in accordance with European regulations, SOX (USA) and GAAP using SAP. Prepare monthly P&L statements for management. Other tools used were BW, EPM, Salesforce, Blackline and Excel Macros.
Interim Team Lead during boss’ maternity leave. Monthly status meetings to address last month’s performance and goal setting. Train team members, monitor workload, responsibility distribution and supplier negotiation. - Reduced time needed for monthly Key Performance Indicators (KPIs) from 2 working days, to half a day and implemented outlook email statistics through Excel macros. Created monitoring tools to manage workload. - Negotiated better payment terms with key UK and Nordic freight suppliers, improving our cash position.
Invoice processing and vendor reconciliation in SAP. Kept track of corporate spending, weekly cash forecast and monthly treasury netting process. Daily communications with suppliers. - Created an excel tool decreasing processing time for transport invoices on average by 30%, eliminated variances between accruals and cost. The tool is now a standard in the finance department. - Analysis of delivery cost in the UK lead to a 5% cost reduction for freight and warehouse.
Building customer relationships and listen attentively to understand customers’ needs and issues to provide appropriate solutions. Excellent communications skills needed to effectively manage expectations, resolve conflict and negotiate compensation. Personal banking product knowledge and precise understanding of UK and global banking regulations required to carry out day-to-day tasks.
Assisted customers through phone solving technical problems. Vast technical knowledge of company services needed, along with good communications skills to guide people through the phone.