LGBTQ Career Community:From data science to product:Lectures & round tables

LGBTQ Career Community:Our main goal is sharing knowledge regarding workplaces across the LGBTQ community: Lesbian, Gay, Bisexual and Transgender, in order to improve our professional lives while creating a safe and friendly environment.We want to share workplaces experience and knowledge as well as receiving recommendations from the community on workplaces which we consider applying for. The recommendations will help us to get a better understanding regarding the potential workplace.

LGBTQ Career Community

Agenda:
18:30 - Gathering, Pizza &amp; Drinks
19:00 - LGBTQ Career Community &amp; Silverfort: Opening words
19:10 - Detecting Obstacles: on the road and in your Deep Learning project. Noa Raindel, Algorithm developer, General Motors.
19:25 - Building products for doctors &amp; patients - product management challenges. Anat Kotler, Director of Product Management, Venus Concept.
19:40 - What can we learn from cyber attacks. Lotem Finkelshtein, Threat Intelligence Group Manager, Check Point
20:10 - Short break
20:20 – Data Science &amp; Product Round Tables
21:00 - Goodbye
 
About the lecturers:
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Speaker: Noa Raindel, Algorithm developer, General Motors
Machine Learning researcher for General Motors, working on Computer Vision for the autonomous vehicle project. Noa holds a master’s degree in Systems Neuroscience from Weizmann Institute. She is a 2 times challenge winner in HackZurich, Europe’s largest hackathon. Noa is also a co-organizer of AlgoIL meetups, a diligent painter and a comedy improv enthusiast.   
Title: Detecting Obstacles: on the road and in your Deep Learning project
Abstract:
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Advanced driver-assistance systems (ADAS) alert the driver in case of any danger. An example for such danger is an obstacle on the road (such as another vehicle, pedestrian, traffic sign, etc.). The aim of my project was to quickly detect such obstacles. One approach is semantic segmentation, which requires many computations and is too slow to perform in real time. Thus, a solution was found in implementing and improving upon StixelNet’s architecture.
In this short talk I will overview the challenge in question, the solution I developed, and some personal insights I gained while working on a first deep learning project.
 
Speaker: Anat Kotler, Director of Product Management, Venus Concept.
Anat Kotler is a Product Management leader with 10 years of experience working in the medical devices industry. In the last 3 years I have also been consulting to early stage startups looking to get into the digital health space. My areas of interest are: digital health products, wearable medical technology, and medical devices IoT.
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Title: Building products for doctors &amp; patients - product management challenges.
Abstract: 
How is it like to bring a product to market in an environment where A/B testing does not exist, but FDA regulation does? How do you collect customer feedback when your customer base is primarily comprised of doctors who are very different from the average app user with time &amp; availability being their major constraint? Can we make medical devices a little less stressful and a bit more delightful for doctors and patients who are using them? Join me in this talk to learn more about the day-to-day challenges of a being a product manager in the medical devices industry
 
Round Tables - Discussion leaders:
Dr. Inbal Budowski-Tal, Director of AI, EverCompliant.
A versatile researcher, heading the AI team at EverCompliant - a FinTech startup in the world of Risk Analysis. Inbal's current focus is in the field of NLP (Natural Language Processing), before that she has worked in the field of Bioinformatics at a pharmaceutical startup and before that she conducted research in User Personalization at Microsoft. 
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Round table - How Data Science and Product teams work together? / how to deliver certainty while working with the uncertain?  
When the Product team presents a requirement that initiates a new research project, we start with a plan. We are also well aware of the fact, that according to the results we get as we follow the plan - the plan will change... 
In this round-table, we will talk about ways to deliver a clear and genuine message regarding the research project to the company's business-level and discuss how to manage the built-in uncertainties in our day-to-day work. 
Inbal Gilead, Director of Product, EverCompliant
An experienced data and product professional.
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Working on data-driven products for over 12 years as a developer, analyst, and PM. Data utilization expert, focused on customer value.
Passionate about data-driven decision making, data analytics, and product development.
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Round table - How Data Science and Product teams work together? 
 
Hila Lamm, Chief Strategy and Business Development Officer, Firefly.ai
 Firefly.ai, an automated Machine Learning company, making AI accessible to all.  A veteran of creating AI based applications, specialising in generating growth with innovation, transforming concepts to reality, driving change in organizations and markets. Previously as General Manager at NICE, Hila was spearheading product, marketing and strategy for analytics solutions based on speech recognition, NLP and voice biometrics, working with Fortune 500 companies. 
Round table - How Data Science and Product work together? 
 
Adi Polak, Senior Cloud Developer Advocate, Microsoft. 
Adi Polak is a senior software engineer and a developer advocate at Microsoft working on Azure, where she focuses on microservices architecture, distributed systems, real-time processing, high scale and performance, big data analysis, machine learning at scale and functional programming. As a developer advocate, Adi brings her vast experience in tech and help both startups and enterprises to design, architect and build their software and infrastructure with cost-effective, scalability, team knowledge and business market in mind. 
Adi was nominated to be 1 of 25 influential women in Software Development by Apiumhub.
 From a software developer to a data scientist – the endless road of decisions in tech, where should I go next, what should I do, is it realy something that I want? And if so, how do I do it? Come join an open discussion about the oppurtonities for data science with a background in software development, what are the challenges and what managers think about it.
 
 Yonatan Dolan, manages Product and business development in AIVF. 
Yonatan has over 10 years of experience in product management and business development in Data Science and AI products; Yonatan had a long carrier at Intel, AI competency center where he successfully led various Big-data, ML based products and solutions in various domains, from IoT and supply-chain to healthcare and pharma. Earlier this year, Yonatan made a radical shift from a big corporate and joined AIVF, an exciting startup revolutionizing the IVF and fertility market. Feel free to grab my LinkedIn one.
Round table - Which questions every Product Manager should ask about Data.
In this session we would discuss the fascinating intersection between Product management and Data-science focusing on products in which the core product value is derived by successfully applying AI and ML. Yonatan would help facilitate the session, but believes a live discussion and real life examples from people experience would make it even more fruitful.
Bar Vinograd, Machine learning consultant. Bar is an consultant in the fields of machine learning and and deep learning. He has been working closely with startups, corporates for the past 6 years. His fields of expertise include Computer Vision, NLP, Statistical Modelling and Anomaly Detection. Bar also has 8 years of experience in software engineering and is a graduate of 8200 IDF Unit and of Tel Aviv University in Math and Linguistics. As an active community member, Bar is frequent at giving talks about machine learning, a global mentor at Google Launchpad and a teacher and an advisory board member at Israel Tech Challenge. Round table -  Opportunities for ML solutions and how to find them.
The road to AI: Fitting a models is the least important part of an ML project. Any software developer can kickstart an ML project. We will discuss how to do that and share experiences on democratization practices in ML.
 
Yair Mazor, Head of data science, Windward.
Yair is heading the data science at Windward. His efforts are divided between developing Windward's data science infrastructure and developing ML models to understand what is happening on the high seas. Yair got his M.Sc from the Weizmann Institute developing algorithms for DNA composition. He also really likes to hike, play chess and falling asleep while trying to read a book. Round table - How to manage a DS project:
The vast majority of DS projects do not make it into production or if they do, make little impact. We'll discuss how to improve the chances of such a project to succeed while addressing the different stages in the life cycle: Design, Implementation, Integration and Monitoring. How to be aligned with the business from the start? how to fail fast? and more.. 
 
Maya Bar-Sadeh, Engineering Director, Philips. High performing, innovative, deliveries oriented Manager with over 15 years of experience in development and improvement of multidisciplinary systems and products. With highly developed leadership and management skills, I bring expertise in driving product strategies, building strong teams, engaging people around ideas and leading complex products from inception to roll-out. Round table - Lead the way. Let's talk about leadership, How do you become a manager? How do you empower your people? Working with global teams, managing in corporates, setting direction toward an inspiring vision Join Maya in " Lead the way" table. 
 
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Silverfort

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