Solvo.ai is an early stage startup developing AI-generated SaaS-based pricing recommendations, providing decision-making support to the logistics sector. I joined the team as a data scientist, and moved into product as a result of my customer-centric focus, to ensure that Solvo’s technical development provides tangible value to users and customers.- I proposed and introduced product management functionality, and developed product roadmaps and clear criteria so that features were sustainably delivered around commercial viability, technical feasibility and feature desirability. By regularly conducting user research to validate our end-users and their pain points, my value proposition has refined our product offering and guided the longer term product strategy, which have been vital in the preparation for Series A funding. - I worked with the CTO to define a technical innovation pathway that timeboxed research projects tied to the practical customer needs, so that we retained our unique positioning whilst also delivering tangible value. This led to the introduction of MLs models that required less customer data, and increased our addressable market by 160% while also simplifying customer engagement. - I developed our value proposition, which was used by the Commercial team to restructure their pitch and led to an 300% increase in customer lead engagement and qualification. I worked closely with the SVP of Commercial to define a value case structure that formed the basis of customer negotiations and pilot scope, which standardised customer qualification and technical analysis, reducing the time for value case analysis from 5 days to a few hours.

I was recruited to improve the productisation of research prototypes, and quickly established rapport with three research teams in the Applied Sciences group, working with them to increase their ability to quickly prototype as well as improve overall communication and ways of working- I introduced processes to increase efficiency of the productisation of research prototypes, through revising communication structures and introducing standards such as Technology Readiness Levels (TRLs). This improved the prototyping and live-testing phases of projects, while also improving visibility with Platform, Legal, Delivery and Product, to ensure alignment with regulatory requirements and quality assurance.

I joined Secondmind (formerly Prowler.io) as a data scientist, creating decision-making applications across logistics, education and finance. In recognition of my ability to bridge technical and customer work, and my success in communicating with multiple stakeholders, I was quickly promoted, where my priority was redefining the Data Science and ML strategy to be more delivery focused. In early 2021, the company pivoted into the automotive industry.I developed my team of 15 MLEs and data scientists to build on their research strengths to have a stronger application focus, through revising the delivery process for projects, introducing accelerated prototyping and creating new technical leadership structures which I supported through personal mentorship. My introduction of differentiating between technical and team leadership increased the retention and engagement of team members, improving customer delivery and acceptance. More specifically, my work in this role included: - Identifying how prospective customer painpoints can be addressed by our technology, and illustrated what this change and impact could have, including customers that were looking to start their AI transformation- Engaged in engineering leadership and operational direction and planning, and directed initiatives such as standards for toolboxes in production- Initiated a machine learning strategy that consolidated our ML technology for the translation of research to customer challenges- Proposed and led development of customer engagement process that culminated in cross-functional representation, spanning commercial, product, user, technical and operational considerations- Growing the ML and data science teams, where I have improved the process for recruitment. I instigated a PDP process that has been taken up and championed by direct reports- Advised legal in technical details during the drafting of formal contracts

- I worked closely with Research, Legal and Commercial, to utilise research prototypes in customer projects. I led technical authorship of SOWs and defined project scope and evaluation for commercial projects, and introduced internal problem-centric hackathons with the Research team, resulting in 3 new lines of research.- While working as data scientist/tech lead, my team’s project led to a potential 75% reduction in operating costs for our clients. Additionally I identified a novel ML-application that linked their strategic and operational aims, which had fantastic feedback from the customer. I presented our work at international events and conferences (including ODSC and CogX)More specifically:- As tech lead, progressed project from initial talks through to POC and POV phases, which were greenlit into a live-prototype phase- Coordinated between customer, business development, technical and research teams through weekly client calls, steering committees, and end of phase presentations- Proposed several additional technical opportunities that were confirmed by the customer as adding value, which were added into the live-prototype and future direction of the project- Mentored other data science who were working on further two client projects, from technical methodology through to project narrative- Promoted our work at industry events including CogX and ODSC, as well as networking events such as Women in Tech - Engaged in engineering leadership and operational direction, including data standards and process

The Optima Consortium, in collaboration with the World Bank and UCL, focused on developing and applying tools to determine optimal allocations for public health. I led the technical development and project management across a cross-functional team, consisting of mathematicians, epidemiologists and health economists, focusing on tuberculosis as well as scoping and prototyping projects for two new disease areas. Team Leader & Senior Analyst, Population Health & Optima Consortium for Decision Science / MAY 2016 - JAN 2018Developed novel application to guide the optimal allocation of $250m USD funding for TB health programs. I met with national stakeholders (including national ministries of health) to identify focus areas and constraints, which increased their acceptance of our analyses. I introduced processes to reduce development time of projects by 25%, and my experiences fed into the best practices and modelling standards for an international TB modelling consortium (TB-MAC). More specifically, • Actively involved in code development of both model and surrounding framework in Python, and introduced modules for bespoke graphics which dramatically improved client acceptance; • Externally represented Optima TB at international meetings attended by public health officials, where I explained and demonstrated our approach, which led to 4 countries approaching us with the intention of working with us to quantify their TB burden and guide the allocation of their TB budget.Within the Infectious Disease Modelling Group, • Generated and scoped projects across new disease areas and applications, which represented a new application direction within the Optima team that has since been implemented;• Supervised and mentored junior staff, including a Honours student who achieved a High Distinction grade• Strengthened our team by recruiting staff to grow technical capabilities

Theme: Matching of opsin (protein) function to neural dynamics for optogenetic control of gain modulationCompleted within the Neural Coding Laboratory within the Department of Bioengineering.• Developed a Python framework that that could emulate detailed experimental stimulations, and used this tool to designed novel neurostimulation protocols using opsins (light-sensitive proteins). I published and presented this work as a featured speaker at an international conference (CNS 2014).• Successfully applied for prestiguous Marie-Curie EU Fellowship for myself. Additionally proposed concept for a successful BBSRC grant, fo interfacing opsin models with existing simulation tools to translate optogenetics to a systems level. This resulted in the PyRho toolbox, a web-based portal for testing functionality • Mastered experimental techniques so that I could test the predictions from my simulations, including patch-clamp electrophysiology and viral injections, in collaboration with Dr Stephen Brickley’s group in the Dept of Life Sciences. • Proposed a novel bio-inspired machine learning rule to explain how neurons could correctly identify spatiotemporal signals in an unsupervised manner, and presented this at an international conference. This work done with Dr Claudia Clopath and the Computational Neuroscience Lab. • Coordinated an inter-group journal club, spanning both experimental and theoretical neuroscience groups, with consistently high attendance and a varied program, including• Mentoring of 2 PhD and supervision of 3 Masters students to develop a computational framework for the design and testing of protocols for neural therapeutics.• Designed and lectured Python section for “Computational Methods for Bioengeering” (postgraduate) • On the strength of my research and tutoring, I was invited to be a tutor at the international summer school Advanced Course in Computational Neuroscience (August 2014, Frankfurt, Germany)

Please see details of the position above (Marie Curie Fellowship)

I worked with groups of clients teaching them how to code and how to use their data effectively. Clients were often from corporate backgrounds, and typically had little to no background knowledge. Over an entire day, we led clients through coding a geospatial web app, using HTML, CSS and Javascript. We gave an overview and context for the workings of the web, through to hands-on coding and practical examples. Given the ambitious nature of the day (an 8hr plunge into the deep!), it was really important to keep the group's interest and energy going, which I was able to do by tailoring and adjusting to the clients’ needs, interest and background.

In this 3 month project at the Dept of Computer Science and Engineering, I extended a project to improve the classification of multivariate series to make them temporally invariant, using k-means classification and temporal warping. As part of this project, I proposed an addition which led to a significant improvement in speed and performance of the algorithm.

I led a small team on two key projects, writing software for financial services to perform large-scale, secure online payment processing as well as an automated reporting service, using Java. We worked to a tight timeline and completed the project on-time, due to my regular communication with the client and attention to detailed aspects of the projects.

I initially started writing programs for solo small self-contained projects while still an undergraduate student, and was responsible for: determining specifications, design, code, verification, validation and maintenance. Later, I was asked to work on larger projects with the rest of the IT team, including tool development for their GlycoSuite database and interface, as well as redesigning their web-based in-house entry software.