Company Specific Flashcards

1
Q

How would you clean data?

A
  • EDA
  • Normalization
  • Missing data (impute, replace, remove)
  • Unify formats
  • Deduplication
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2
Q

What kinds of datasets/variables have you worked with?

A
  • Large financial datasets

- Dirty data

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3
Q

s

When was a time where a group had one idea, and you convinced them otherwise?

A
  • Data Strategy
  • Agile Scrum
  • Team Health check
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4
Q

Tell me about yourself.

A

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5
Q

How do other people around judge you?

A
  • How I treat people

- Work ethic

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6
Q

What is your weakness?

A

‘Done is better than perfect’.

  • imposter syndrome
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7
Q

Why analytics?

A

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8
Q

Why deloitte?

A

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9
Q

Talk about 2 of the biggest challenges that Deloitte is facing.

A
  1. Relentless pace of tech innovation (Simon Murphy, Silicon Republic) - placing bets on what technologies will be front and centre in the future
  2. Serving clients in industries with aging business models - more competition, leaner, customer expectations
  3. Talent
  4. Responsible AI
  5. Data Strategy
  6. GDPR, Open Banking – consortium, federated data, homomorphic encryption
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10
Q

Where do you see yourself in 5 years?

A
  • People management
  • Acquired experience across industries
  • Polymath
  • Leader
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11
Q

Examples of Deloitte innovation

A
  • TAXIE - Tax division RPA
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12
Q

Krawler

A
  • FirstSense
  • Deep web, dark web, darknet crawling platform
  • Scrapes stolen credit card information
  • Matches partial cards to customer database
  • Saved millions in $
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13
Q

Rule Evaluation Framework

A
  • Saves $200,000 per client evaluation
  • Developed using Python and R
  • Simulating performance of rules on real customer data to see what benefit it could bring clients in production
  • optimizing thresholds across a number of metrics - recall, precision, false positive rate, false positive amount, cost ratio, alert rate
  • catered for many rule types (list-based rules, regex rules, threshold rules, rules with profiles)
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14
Q

Automated reporting framework

A
  • Python data validation, pulling
  • R ggplot2
  • Docker
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15
Q

Leadership examples

A
  • Foroige volunteering - Big Brother Big Sister programme
  • CoderDojo
  • YoPro
  • Mentoring at work
  • Agile Scrum Advocates group
  • Kanban Master
  • (Anaplan) - Taken the lead to improve interview processes, speak at recruitment events, first day at women in technology fair
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16
Q

Challenges

A
  • Communicating poor model results back to interested senior leadership
17
Q

Fraud Modelling

A

Thirty party vendor partnership
Interactive tool for modelling
Sharing compute resources
Increased recall by 40%, precision by 5%

Now Python based modelling, customized decision tree, neural networks, building an ML pipeline using DAGs to set off execution events

18
Q

Most important learning

A

Data Science should be treated with same reverence as software engineering

19
Q

Strengths

A
  • Curiosity
  • Adaptability
  • Humility
  • Push myself in and out of work