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Since December 2021
Instructor since December 2021
Practical Data Science - Interview Preparation - Working backwards from End Product with Machine Learning
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From 89.37 C$ /h
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Class teaches you how to apply your math and coding skills, and prepares you for the interviews with the companies ranging from start-ups to the big tech giants. It covers ML part: (i) working backwards from problem statement with Data Science eyes, (ii) choosing the best Machine Learning approach, (iii) drilling down in ML, and interview preparation part: (iv) preparing you to answer tech questions, (v) ML concepts questions, (vi) product questions, (vii) work principles questions.
Location
location type icon
Online from Belgium
About Me
I'm a Sr Data Scientist | Sr Product Manager with 9+ years of experience in Amazon, Generali, and PWC. I am passionate about building strategies, launching products, and democratizing AI knowledge.
Education
Master of Science, Mathematics (Probability and Statistics) from University of Belgrade, GPA 9.75/10
Bachelor of Science, Mathematics (Probability and Statistics) from University of Belgrade, GPA 9.28/10
Experience / Qualifications
Amazon EU, Italy / Belgium
Sr Product Manager Expansions (July ’21 – present)
• Owner of the Selection and Vendor management strategy for Amazon expansion into a new geography, creating and
executing on a first-time-at-Amazon scalable and lean operating model for expansion
• Single-threaded leader of the Catalog Localization for 5 new countries launch across 3 continents
Amazon EU, Milan, Italy
Sr Data Scientist | Sr Product Manager (July ‘19 – June ‘21)
• Leading a team that uses Machine Learning to introduce step-change in Amazon EU Retail business by incubating
innovative ideas that improve customer experience and optimize the way business is managed.
• Projects: Automatic root cause detection using NLP on customer feedback (return rate reduction 24% using ML
classification and unsupervised insights extraction); Causal inference pipeline for Promotions propensity and
incrementality (>1000 business users, yearly savings 12%); Price erosion prediction along the product lifecycle
• Tech stack: Python (spacy/BERT, sklearn, Keras), AWS (Lambda, Amazon SageMaker, s3, QuickSight)
Generali Group, Milan, Italy
Sr Data Scientist ( March ‘16 – June ‘19)
• End to end Data Science projects: Business case setup, guidance on analytical process, product implementation
• Business areas: Process optimization and automation, Fraud, Claims management, Health insurance, Pricing
• Machine Learning algorithms: Deep Learning with Keras, Computer Vision, Natural Language Processing, OCR
• Tech stack: Python, Spark, R, Microsoft Azure, Spotfire, Tableau
Generali insurance Serbia, Belgrade, Serbia
Actuary (June ‘13 – February ‘16)
• Pricing and product development for property and casualty insurance, modeling of new tariffs for non-life insurance
• Pricing risk assessment, Reserving risk assessment, and technical reserves calculation
• Forecasts and business plans: best estimate of liabilities
PricewaterhouseCoopers, Belgrade, Serbia
Intern in Assurance (September ‘12 – March ‘13)
• Team member on audit engagements of financial statements: audit of business processes, design, and operation of
internal controls, performing substantive testing
• Assessment of loan loss provisions in the financial services industry
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
English
Italian
Serbian
French
Availability of a typical week
(GMT -04:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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Helping you understand the logic behind the code

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4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

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How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

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Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

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Good-fit Instructor Guarantee
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Contact Katarina