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Irene Bratsis 

Product Manager | Data Scientist | Machine Learning & AI | Analytics | Consultant | Data Strategy | Diversity & Inclusion Advocate | Advisor | Speaker | Writer

Data Science Product Manager at Beekin

Women in Data NYC & Boston chapter lead

Women in AI Director of Special Projects - WaiACCELLERATE, WAI Hackathon, WAI Awards (10/2021)
Women in Trusted AI founding organizer, event and speaker outreach organizing events around trust and ethics in AI (affiliate group of Women in Cybersecurity)

Writer for Women in Data, Medium, Towards Data Science, Analytics Vidya, Gesture, An Injustice, MLearning.ai, CodeX, Data Driven Investor and personal articles on LinkedIn 

 
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Hello!

I currently work as a data science product manager at Beekin, I lead the Boston and NYC chapters of Women in Data, I'm a founding member of Women in Trusted AI and I organize events and look for speakers for our Women in AI WaiTalk events.


I love meeting new people in the world of data science & analytics, ML and AI, sharing ideas, collaborating with organizations that are committed to diversity & inclusion, and helping build the fourth industrial revolution as we expand into the data and AI rabbit hole together.


Passionate about furthering women's participation in data and AI careers, I feel it's crucial for us to bridge the gap in this disparity if we are to build a more equitable society that works for all humans. Diversity of backgrounds, diversity of experience and ethics will be a huge focus as AI continues to impact our lives in myriad ways.


Based in NYC 

🐦 Follow me below to stay up to date on articles, events, initiatives and projects


 

― Kahlil Gibran, The Prophet 

“Beauty is eternity gazing at itself in a mirror.


But you are eternity and you are the mirror.”

 
Sunset over the Mountains
 

Professional Experience

Data Science Product Manager

April 2021-Present

Drive ideation, planning, development and launch of Beekin prop tech products including a lease renewal optimization product as well as a rental evaluation product for single and multifamily units. I collaborate with our data scientists, machine learning engineers, devops engineers and designers to build and manage our products and I work closely our ML team to design, scope, optimize and deploy our products.


I am the product owner of our products and oversee the marketing, messaging and communications of our product and I communicate regularly with key stakeholders to manage delivery and go to market strategy of our products.

Data Scientist, Gesture

December 2020 - April 2021 

Identifying valuable data sources and automating collection processes, undertaking preprocessing of structured and unstructured data as well as analyzing large amounts of information to discover trends and patterns. Building predictive models and machine-learning algorithms and combining models through ensemble modeling. Presenting information using data visualization techniques and proposing solutions and strategies to business challenges. Collaborating with engineering and product development teams and serving as cross functional liaison between various internal teams to promote a data driven company culture as needed.

In this role, I set and execute a data strategy for the business to better leverage its data assets, innovate and grow through master data management (MDM); data governance; data analytics and data science and present findings to leadership directly.

Director of Special Projects, Women in AI

November 2020 - Present 

Women in AI (WAI) is a nonprofit do-tank working towards gender-inclusive AI that benefits global society. Our mission is to increase female representation and participation in AI. We are a community-driven initiative bringing empowerment, knowledge and active collaboration via education, research, events, and blogging. I am most focused on community building, growth, speaker outreach and social for monthly webinars for the US branch of WAI which was founded in Paris, France.


In addition to that, in 2021 I will also be directly managing the WAI North American Awards & Hackathon for 2021 which is a continental effort geared towards female led startups in AI and data in Canada, the United States and Mexico. WaiACCELERATE is an accelerator program for Women in AI which will nurture and support female led startups and I am managing the Hackathon which will be the culminating event of WaiACCELERATE. Startups that will move on to receive their awards after the WAI Hackathon will do so at our WAI Awards ceremony in September for all of North America.

Chapter Lead, NYC & Boston, Women in Data 

October 2019 - Present 

Relationship building, advising, partnership managment, event coordination, book club, blogging, speaker outreach, community building for Women in Data, a non-profit that works to increase diversity in the data-driven sectors. As a member for the leadership committee for the New York & Boston Chapters, I help organize our bimonthly webinars and other internal initiatives.

In 2020, we moved our events entirely online, grew our NYC community by 74% and moved to a membership business model. I oversee the event organization for NYC and Boston, speaker outreach and prep for the events and grow partnerships with local organizations that want to collaborate with us. In 2020 we had collaborations with Filtered.ai and Society of Woman Coders as an effort to combat bias and diversity lack within the data community.

Data Scientist, Thinkful

May 2019- July 2020

Learned industry best practices and practical software development standards with a focus on Python, SQL, algorithms & data structures.
Created and deployed data science projects across a variety of industries and content while learning new languages and frameworks by collaborating several hours every week with a senior data scientist to ensure proficiency across a variety of models, tools and algorithms.

Acquired technical skills including Python, Statistics, Numpy, Pandas, Probability, Data visualizations, data exploration and evaluation, storytelling with data, SQL, Postgres, Seaborn, Data, Cleaning, Imputing Data, Data Validation, Experimental design, A/B A/A testing, Selection bias, Null hypothesis significance testing, Supervised & Unsupervised learning, Structured & unstructured data, Feature engineering, Accuracy & Error types, classification & regression algorithms, Natural Language Processing, KNN, Random Forest, Decision Trees, Ensemble modeling, Support vector machines, Boosting models, Clustering & K means, Deep learning, Neural networks, Perceptron models, Supervised & unsupervised neural networks, Web scraping, Hadoop and big data storage, Spark

Revenue recognition, customer experience, sales, operations, dashboard optimization for relevant markets, alternating between assisting sales and delivery organizations as directed by leadership. Influenced over $150M in revenue during the course of the year. Accelerating the world's transition to sustainable energy!

June 2018- June 2019

Sales Operations Analyst US Consultant, Memsource

November 2017 - June 2018

Positioning translation management solution to enterprise customers, mapping out territories and go to market strategy, pipeline building and outreach, market analysis, exploring the localization industry from a tech perspective and providing sales ops analysis to streamline internal processes for an AI-powered, cloud-based SaaS translation platform used by global companies, agencies, and translators.

 
Open Field

Projects

Showcase of Work

 
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World Happiness Report

Trends, analysis, explorations and further research proposal for the most recent World Happiness Report which is generated by United Nations Sustainable Development Solutions Network.
All data cleaning, exploration and analysis was done by me using python 3 in a Jupyter Notebook and all data visualizations were done using Matplotlib, a visualization library in python.

Supervised Learning: Predicting Ether Prices 

Can we uncover a model that best predicts the price of ether based on ancillary data collected in our data set? Let’s start with a few of the most pressing highlights inherent in the data during data exploration. I will be taking a look at Ether pricing data and doing discovery, exploration, cleaning and general analysis through visualization, pair plots and correlation matrixes to get a sense of the data before going into the models. 

Presentation slides can be found here.

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Tableau Public  

Tableau Public stories of things I'm generally interested in analyzing and visualizing

Unsupervised Learning: MovieLens Data with K means Clustering!

Netflix wants to identify similar movies based on movie characteristics and this dataset fits the bill. I will be using K means clustering to hone in on some of those similarities and looking for patterns that can help us recommend movies to users based on user behavior, ratings and genre analysis of clusters using Python 3, Seaborn, Matplotlib and Pandas. Other clustering models used: GMM, Mean Shift, Fuzzy C Means, HDB Scan.

Presentation slides can be found here

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Supervised Learning: Predicting the Price of Avocados using OLS 

The goal here is to find a model that would accurately forecast Avocado prices. I’d like to also ensure the information is sufficient to effectively forecast and determine if there are any sort of patterns in our data before building models. Selecting a model and choosing the best performing model for our data and parameter tuning that model to make sure it's effective enough to make predictions.

Presentation slides can be found here.

Zillow Sale & Rental Market Analysis

Zillow Research aims to be the most open, authoritative source for timely and accurate housing data and unbiased insight. My goal is to empower consumers, industry professionals, policymakers and researchers to better understand the housing market, in this case, I am comparing two markets, Boston and New York. The project goals are to bring visibility into the rental and housing market in NY and MA and to shed light on the factors that would impact the decision to buy given projected performance of that investment into the future using economic data from the Federal Reserve as well as publicly available Zillow data.

Presentation slides can be found here.

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If you work in the field of Machine Learning, Data Science, AI and Analytics, and you're part of something exciting that you feel needs to be shared, say hi and connect.


I want to nurture a robust community of entrepreneurs, practitioners, professionals and curious people. I want to build with critical, creative and strategic thinkers that enjoy connecting, collaborating and best of all: discussing ideas.


If you work for an organization that wants to partner or sponsor Women in Data, Women in AI or Women in Trusted AI please send inquiries here

Info

NYC

+1.781.652.1402

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