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PROBLEM: Typically, founders that are bootstrapping their startups usually take the "hands on role" early in their startups which means they had to perform manual kyc documentation checks on Dojah platform. Some founders did not have technical know how knowledge on how to consume APIs from the identity verification widget which created a lot of ambiguity, drop off, customer retention lagging for Dojah SOLUTION: Using the identity verification widget API, I created a flow chart, PRD and use case scenarios that includes parameter of inquiry needed to call Dojah ID widget APIs if the API were consumed by the customers, then used the inquiry parameter as information fields for the no-code tools.
Using the no-code tools, when a founder enters the inquiry parameters with information provide by their end users, at the backend it is automatically calling the ID widget APIs without these founders/operators knowing. At the frontend, the response from the API call is presented to them without needing to consume the APIs.

PROBLEM: Using Amplitude and moseif tools, noticed that there was a lot of drop off early on at Dojah especially during compliance stage. Compliance requested for documentation via mail which created room for a lot of forth and backs with the customers. SOLUTION: Reworked the onboarding at Dojah, once users does the first set of information signups which includes email, company name and product you want to use, they are directed to calendly to book a demo of our product. The personal touch from sales during demo, they will more likely want to use the product. After demo, they are sent automated link to complete their sign up which includes uploading compliance document on their dashboard. At the admin dashboard, the compliance analyst views the documents. If the documents are good to go, the approval is toggled. Then user is verified to use our services.

PROBLEM: There was a problem of FX liquidity and inability to use some of our cards on 18yers+ platforms such as casinos at CashEx SOLUTION: As a remittance (CashEX) they were disparities in FX rates which caused a lot of ambiguity to users as we were offering different rates within a day which is meant to be a fixed rate for the day. Proposed an integration with an international FX partners which includes the business margin spread that when a user needs a particular currencies and the user has been deposited money for the exchange, it calls our partner APIs from our platform. Also created a status page for those APIs so we can be informed when there is a outage and inform our users. This improved user experience and stability in the business

PROBLEM: There was a problem that web2 users could not onboard easily on web3 platform because of the complexity with the process at Fly wallet SOLUTION: Not a lot of travelers are web3 enthusiasts which kind of created a segmented market for FlyWallet. So I created documents and flowchart that will enable users that have Google account, Facebook account and Apple ID to onboard easily and save for travels ahead. Created multiple integration partners and monitored the API wrappings by the engineers. This created a diverse customer base for the web3 travel tech company.

Went through a full startup routine in creating the product, starting with generating ideas through ideations and co creations, making mind maps of them on Miro, leading it to a finished tech plan for developers and making it into life. Reviewed design via Figma and made testplans with researchers to verify ideas on target audience. Made integrations with bank front and back systems to aggregate data and made profit of it for our clients with complete analytics of their finances and budget that makes no one at the market right now together with banking functions.

Tackled the learning issue by implementing various personalized learning styles and improved the product checkout process by decreasing the clicks required for payment from 4 to 2 through A/B testing. This streamlined the checkout process, making it more efficient and user-friendly.

I've used my strong background in Product Marketing to help boost our company's reputation in the Edtech sector. Leading a talented team, we've come up with fresh growth ideas especially suited for new companies like ours. In the changing world of Edtech, I've always aimed to make sure Careerist keeps growing and outdoes its goals.

By establishing an intricate performance monitoring system, transformed raw data from marketplace APIs into actionable insights, visualized on BI dashboards for executive decisions. Recognizing the specific needs of our top three sellers, with a combined annual revenue of approximately $10 million, crafted a specialized financial tool to streamline their logistics and facilitate their expansion into global marketplaces. Developed a novel cross-client analytics mechanism, a move that phenomenally tripled our client intake in a month, underscoring the company's adaptability and growth potential.

Managing cross-functional teams and facilitating collaboration across diverse program positions. I act as a bridge between technical AI teams and various business units ensuring seamless integration and deployment of AI solutions. I manage third-party applications to make sure AI tools work smoothly, using tools like EazyBI to make key metrics, Google Sheets Excel and specific charts for Agile teams.

Over a three-year period, we worked closely with one of Russia's largest banks to successfully establish a robust AI monitoring system. This system operates with precision to swiftly address system failures by pinpointing their root causes and making well-informed decisions. The model expanded the scope of forecasted indicators to a total of 50,000, achieving an 85% reduction in false positive alerts. It also delivered a 20% improvement in precision compared to previous methodologies.

Led a team to develop an AI monitoring system that was capable of detecting anomalies in various business indicators. By closely working with one of Russia's largest banks over a span of 3 years, we designed and implemented an advanced backend system dedicated to the detection of anomalies across a wide spectrum of critical business metrics. This system significantly decreased the time and effort previously invested in manual incident detection and enhanced operational efficiency.

I coordinated a team in the development of two distinct automated incident detection models, which yielded substantial improvements in incident detection times, reducing them by an impressive 5 to 15 minutes. These models played a pivotal role in streamlining processes and optimizing the bank's operations.

As a computer vision expert, I was consulted to address the challenge of facial pore recognition for a beauty application. The problem involved accurately identifying and measuring individual pores on various skin types and tones. To tackle this, I designed an algorithm that could adaptively capture high-resolution images and extract relevant features. Leveraging machine learning, I trained a model on a comprehensive dataset that I helped gather, comprising different face images under various lighting conditions. The resulting algorithm could recognize and measure individual pores with high precision, providing personalized skincare recommendations to users, and thereby enhancing the app's user experience.

In the corporate sector, text data flows swiftly, emanating from diverse sources in various formats, destined for multiple endpoints and actions. This continuous stream of data necessitates management through structuring, making it easy to interpret and analyse, and ultimately directing it to the appropriate user or applying the insights obtained. I utilised several large language models (LLMs) streamline and enhance this process:

• A question-answering LLM, enabling users to inquire about the content of each document within the flow.
• A summarization LLM, offering a concise overview of each document.
• An NLP model for sentiment analysis, determining the sentiment of each document.
• An NLP model that calculates an absolute similarity score among all documents.
• Clustering, which groups similar documents together and simultaneously identifies outlier documents.

In the dynamic realm of banking, ensuring effective anomaly detection is paramount. Utilising my expertise in state-of-the-art ML algorithms and advanced Deep Learning techniques, I tackled this imperative at Deutsche Bank, a globally recognized leader in financial services. The solution involved several critical stages:

• Identifying relevant data from various sources.
• Researching and testing optimal Machine Learning algorithms for tabular data analysis.
• Exploring and validating appropriate Deep Learning models for extracting insights from textual data (Natural Language Processing).
• Integrating the resulting models into the rigorous banking environment, complete with all necessary software engineering components: Python, Object-Oriented Programming, Algorithm Design and Complexity, Deployment, and more.

Market research is a domain teeming with diverse data types, including tabular, audio, and particularly text. This wealth of data can be utilised not only to enhance internal processes through automation but also to equip the end client with valuable new functionalities and use cases.

• The objective of this use case was to automate the labelling of open-ended questions from online market research surveys. Open-ended questions, which receive text responses, require further post-processing to extract useful information. I automated this process for one of the world's largest market research firms using a series of interconnected techniques:
• Utilising various forms of word embeddings, leveraging both Neural Network models and general techniques.
• Implementing clustering to group similar responses.
• Applying summarization and content extraction methods to distil and highlight key insights.

Our users received profit for selling their goods and services via our payment gateway in crypto. Many of them wanted to convert this profit into fiat and withdraw it to their bank account. Previously we used a 3rd party service that had a $30.000 threshold for crypto-to-fiat transactions, which prevented our new clients from giving us a try since they couldn’t trust us enough with the amount this high, and SMBs couldn’t afford keeping this amount frozen for so long. I researched and led the integration of alternative service with their API and our UI, that allowed us to lower this threshold to as little as $20 and onboard large clients that previously used our competitor.

The admin side of our service was outdated, lacked cohesiveness due to the initial desire to accelerate time to market, and there were constant complaints about things being hard to find there and simple tasks taking hours. Our business development managers also noticed that users often required personal help with onboarding during calls and wrote mailed similar questions to our support, which meant spending extra time and money. I led the redesign of 14 sections of the personal account, including the payment history, invoices/donations pages, mass payouts, balance top-up/withdrawal, wallets management, and various integrations.

Leveraged best practices to ensure seamless and quick sign-up and onboarding for new users.

Leveraged and combined several payment management systems in the mobile app to receive the desired payment flows: user-platform-user, user-platform-merchant, merchant-platform-user. Optimised the usage of 3rd party APIs and their sequence to receive the lowest possible fee.

Led the creation of an admin panel for a new social media startup from scratch. Took part in creating the project documentation, design, backend and frontend services with a product team. We created the means to manage content and its categories (with a 3rd party tool that helps moderation + the work environment for human moderators), users, payments (payment history, integrations dashboard), notifications, and editorial pages.

As an iOS developer in FG BCS, a leading investment company in the Russian market, I contributed to their app for private investors. I developed a transaction history feature that allowed users to have a full control of their cash flow budget. I implemented an infinite scroll and comprehensive filter. Users could find the details of each transaction in the modal view and get the summary of their expenses and earnings as a stacked bar chart.

As an iOS developer in FG BCS, a leading investment company in the Russian market, I contributed to their app for private investors. We wanted to enable our users to top up the card balance with Apple Pay. There was no direct way to implement this flow, so we had to leverage a third-party service that created an order for the refill amount and transferred the payment to the client’s card using our API. The transaction processing and balance renewal on the screen were imitated.

As an iOS developer in FG BCS, a leading investment company in the Russian market, I contributed to their app for private investors. I implemented a flow of adding cards to Apple Pay. The process involved users selecting their bank card, going through the Apple native flow, and showing the result to the user on our side after receiving positive feedback. The tricky part was getting approved by the Apple commission who checked our solution for compliance with their regulation, which we successfully passed.

As an iOS developer in FG BCS, a leading investment company in the Russian market, I contributed to their app for private investors. I implemented a flow of transfer money from a user's account or cart to a card of another bank: users selected an account or a cart to transfer money from, next step they entered card PAN or a phone number and selected another user’s bank from the suggested list.

As an iOS developer in FG BCS, a leading investment company in the Russian market, I contributed to their app for private investors. User was able to enter parameters of an account or a card they wanted to create (currency, type, name, etc) and after confirmation refill it from another card, account or with Apple Pay

Managed and scaled: ⁃ Trading products ⁃ Buy and sell ⁃ Crypto and NFT products ⁃ Alternative payment methods, digital wallet payment methods ⁃ Bank payments (instant, local in UK) Oversaw partnerships, strategy, GTM, and distribution channels Managed a team of 20+ product managers and mentored junior PMs

Socials growth

• Telegram: 0 -> 3200 subscribers
• Discord: 0 -> 7000 server participants
• Twitter: 0 -> 1400 followers
• Telegram chat: 0 -> 1900 chatters Achievements
• Successful launch of 15 partnerships (crossmarketing and paid promotion from our side)
• Launched LATOKEN NFT native collection Angry Chicas and developed a landing for it
• Setting up the whole content structure with weekly events, streams, raffles, crossmarketing events.
• Created and launched LATOKEN volunteer program called LATOKEN Marshals
• Launched merchandise with LATOKEN Angry Chicas NFT Collection
• 0% of negativity in LATOKEN NFT community created by me
• Created the giveaway, raffles from zero via gleam.io
• Developed bots with gamification system, DAO elements and XP system to keep community always active, engaged and entertained
• Launched AR Instagram Mask for Angry Chica with special promo campaign
• Set up the whole analytics process via GA4 for LATOKEN NFT and LACHAIN
• Created a whole set of content topic regarding memes and global events to launchh viral activity and attract young community
• Comarketing with: Kishu Inu, CVSHOT, MNTG, BabyDoge, Adappter, Olympus, and other crypto projects and influencers

Socials growth

• Telegram: 0 -> 4400 subscribers
• Discord: 0 -> 12000 server participants
• Twitter: 0 -> 8000 followers
• Telegram chat: 0 -> 500 chatters Achievements
• Launched LACHAIN NFT rewards system
• Set up the whole content structure with announcements, weekly events, streams and raffles
• Developed design patterns and standards for SMM
• Launched crossmarketing announcements with: Polygon(Matic), Avax, Fantom and QuickSwap.
• Developed a DAO system with XP and gaming features in discord
• Set up targeted ads in Twitter, Facebook and LinkedIn to attract new audience and investors

Socials growth

• Telegram chat: 100 -> 4500 subscribers Discord: 150 -> 1700 server participants
• Discord ER: 0 -> 4.36%
• Twitter: 0 -> 115 followers
• Reddit: 0 -> 3000 community karma Achievements
• Set up the whole content structure with announcements, weekly events, streams and raffles
• Created trailer of the game and promoted it on Reddit with organic reach
• Developed Social Media Pages Design w/ banners, profile banners and icons.
• Hosted 3 AmAs and 1 NFT raffle which brought around ~300 active players to the beta version of the game.
• Set up google analytics to track link clicks and engagement

We developed and scaled a feature for 3P marketplace sellers to facilitate the adoption of the Pan-European FBA, an EU-wide fulfillment program. The feature is a cost-saving calculator that identifies potential savings for sellers by enabling product listings across the EU, thereby incurring lower fees. This was a problem, as sellers had to align 4 different datasets previously, to get this information before deciding whether to join the program. We utilized a build approach, we started from scratch and led the entire process from inception to the development of the MVP and eventually productionalizing the feature for over 150,000 sellers. This unlocked large holdout brands, such as Instant Pot, and generated $100 million in revenue within a year of the MVP.

We developed and optimized Seller and Affiliate features for TikTok Shop. Our primary aim was to revolutionize social commerce tools across Europe. The initiative centered on creating value where TikTok Creators, Sellers, and the platform's 1B+ monthly active users converge. We started from the ground up, steering the entire process from its inception. Our drive was to cater to the ever-growing user base of TikTok, tapping into the potential of both Sellers and Affiliates.

Dating apps, while popular, had users spending excessive hours daily, leading to notable frustration. Research from smalltalks.ai indicated that the average daily usage was a substantial 1.5 hours, with a staggering 70% of users experiencing anxiety post-use. To address this, I developed an application powered by the advanced GPT-4 artificial intelligence. This AI not only mimics human interaction but crafts initial messages based on individual profiles, engages in meaningful conversation, and establishes emotional connections using a meticulously curated pick-up database. Once a rapport is established, it proposes a real-life meeting and notifies the human user, thus streamlining the dating process while reducing user stress.

As CTO at PrimerAI, I developed an intelligent platform for personalized cosmetics suggestions using ML and AI, including OpenCV, GPT-4, and K-means clustering for skin tone detection and categorization. I employed a holistic approach in developing and implementing this web app, which analyzed users' skin tones using computer vision and unsupervised ML algorithms. My contributions covered all aspects of the project, including designing and building the frontend and backend of the web app, as well as administering Linux servers. This ensured a seamless, end-to-end product experience that catered to our users' needs.

GregTraider is a streamlined framework designed for automated online trading and trading strategy backtesting using historical data. It simplifies the process by allowing users to write a single strategy for both backtesting and online trading, eliminating the need to rewrite and adjust strategies for different platforms. The primary benefit of using Gregtraider is that you only need to write and adjust your trading strategy once for backtesting, and it is immediately ready for real trading. This saves you the tedious work of rewriting and testing your strategy multiple times.

At WildFoundry, I held the role of developer advocate, producing educational programming content through articles and YouTube videos while offering client support. A prominent GPT use case from my experience involved analyzing the apps that users ran on their devices. To overcome the challenge of manually examining tens of thousands of users, I developed a script that scraped user pages one by one and analyzed the content via the GPT API before storing the results in a pandas dataframe. This approach enabled an unbiased and comprehensive analysis of user applications, yielding accurate numbers and insights that outperformed traditional survey methods. The ensuing report allowed the identification of target customer groups and guided the company in shaping its customer relation strategy.

In this project, I focused on implementing small open-source models, such as Zephir-7b, on single-board computers like the Raspberry Pi. This setup enabled the creation of a robot controlled by an LLM. Working with such compact models presents numerous challenges; one significant issue is the reduced level of intelligence compared to larger models like GPT-4. To address this, the project required fine-tuning the open-source LLM to achieve satisfactory results.

Over the last two years, I've been dedicated to promoting the integration of AI among scientists at the globally acclaimed marine research institution, Plymouth Marine Laboratory (PML). PML's core research program is focused on critical areas such as climate change, marine pollution, and sustainability. In a recent research study published in July 2023, I developed Machine Learning approaches to ascertain the primary factors influencing the phytoplankton carbon-to-chlorophyll ratio in the Atlantic Basin.These methods provided valuable insights into the environmental factors affecting phytoplankton carbon and chlorophyll levels in the corresponding figures.

With a 9-year tenure as a freelance Audio Technology Consultant, I crafted a wide array of technical solutions. These encompassed the development and implementation of new real-time audio effects for Arm Cortex M4 hardware, the creation of VST plugins with the JUCE framework, the design and integration of a real-time processing system using Beagle Bone Black and the Bela realtime platform, as well as contributions in musical sound synthesis and Machine Learning consultancy for an interactive musical project.

As part of my role at Time Research & Innovation, I Ied creation of World Health Aid (WHA), a multilingual real-time health advice platform designed to support disadvantaged individuals. WHA is a comprehensive solution that facilitates telemedicine services, bridging the gap between healthcare providers and underserved populations. It combines live patient and clinician-facing decision support systems, offering a range of paid and free services, including self-diagnosis screening through video and voice chat, multilingual instant translation, voice-command symptom search, real-time messaging, and secure record keeping.

• Gathered a team of BI Analysts and Developers from the scratch
• Created a data warehouse prototype using Qlik Sense and built a full commercial business reporting system from the ground
• Complete evaluation of the company's business reporting system, including financial reporting. Sources of financial losses and inefficiency have been identified and quantified.
• Provided recommendations to the operations office on sales enhancement activities and monitored implementation, which resulted in a significant revenue impact.
• Built demand forecasting model using XGBoost Regression

• Developed an automated Python Machine Learning model that provides post-evaluation for sales support programs and takes mutual cannibalization into account.
• Organized and led an ETL developers team that built Avon Russia's Cloud Data Warehouse, the only data source with consolidated and verified business data for operational analytics.
• With a team of ETL Developers Rebuilt Russian data instance of Global DWH in Snowflake with attached data flow interfaces.
• Initiated, developed, and implemented new Representative discount system and performance tracking dashboard in Tableau to promote productive sales behaviour
• Initiated and performed audit of existing business planning processes, suggested ways to improve demand forecasting accuracy
• Initiated and performed series of strategic analysis on pricing, average order, representatives’ costs and benefits to understand causes behind current business performance and highlighted possible business solutions to the top management and enabled more accurate sales predictions.

• Created a team and built a business insights and analytics function from the scratch
• Successful converted Marketing Research department to big scale Business Insights function, combining under one roof Marketing Research, Demand forecasting, Trade efficiency analysis & modeling, marketing planning and reporting.
• Successful development and set up of Demand Forecasting process across all 6 markets.
• Improvement of deployed Primary Research methodologies and internal data sources.
• Development and launch of new analytical reporting initiatives.

Led the shift of analytical infrastructure, enhancing data resources and decreasing update errors by 90%. Increased identifiable traffic sources from 60% to 95%. Played a crucial role in reducing fraudulent responses from 28% to 1-4% by strengthening moderation and creating a fraud analytics direction. Contributed to a project reducing the cost of applications from groups by 70% and reducing vacancies in central Moscow from 20% to 6%.

Developed an integrated automated online and offline sales analytics system, utilizing various tools like GA, ExpertSender, Dynamic Call Tracking, Naumen, and 1C. Reduced BigQuery costs by over three times and improved the effectiveness of advertising campaigns by 8-10%.

• Developed a universal platform for AB testing.
• Led the setup of a web analytics system from scratch across multiple websites and a mobile application, including change monitoring and business reporting.
• Built a team of marketing analytics to assess the effectiveness of advertising placements of various partners on the site.

Challenge: Optimization of Retail Space: We have a certain number of shelves in a store and various display zones that must be represented. The task is to fill the shelves in such a way that the chosen layout is maximally effective in terms of revenue and also meets business requirements. Solution: I wrote a library in Python that, based on sales data and store size, calculates how the retail space should be filled. It utilizes a Beam Search algorithm that, at each step, retains not just one best solution, but the top N solutions. This approach allows for diversity (as the best solution at each step does not always yield the best sequence) and maintains a reasonable breadth of search by not analyzing very poor filling options. This solution is automated, simplifying its use for business representatives. Outcome: This solution will be used for zoning in the deployment of new stores and the redesign of old ones