Product Data Scientist - Crypto

Career Renew; London; On-site; GBP 50,000 - 80,000; Full time

Job summary

A leading company is seeking a dedicated Platform Data Scientist to extract crucial insights from complex datasets in London. This role demands a versatile individual with 2-3 years of tech industry experience, proficiency in data tools like Python and SQL, and the ability to drive projects autonomously. A passion for cryptocurrency is preferred but not essential. The successful candidate will contribute significantly to strategic decisions, alongside competitive compensation and growth opportunities.

Benefits

Significant upside

Competitive salary

Negotiable equity opportunities

Qualifications

• 2-3 years experience in a tech consumer product, preferably in a startup.
• Experience managing projects from identification to testing solutions.
• Interest in cryptocurrencies is a plus.

Responsibilities

• Extract meaningful insights from complex data sets.
• Oversee project management from ideation to rollout.
• Collaborate to measure impact of changes and identify trends.

Skills

Autonomy

Detail-oriented

Data visualization

Statistical analysis

Causal inference

Tools

Python

R

SQL

Bigquery

Graphana

FullStory

Dataform

Job description

Career Renew is recruiting for one of its clients a Platform Data Scientist - this is a fully onsite position in London UK.

We are in search of a high-performing Data Scientist who is passionate about extracting meaningful insights from complex data sets. This individual will play an integral role in helping us understand our growing data, which will directly influence our strategic decisions. The ideal candidate is nifty, detail-oriented, and has a keen interest in exploring and unraveling complex data trends. Familiarity with the world of cryptocurrencies is a plus, but not a requirement.

In general, we want an experienced, flexible, and driven data scientist who has a broad range of knowledge in the field rather than a specialist who has focused on a specific area. They need to be autonomous and self-motivated, who can work with other teams to measure the impact of changes, as well as identifying problems, then developing and implementing solutions.

Requirements:
- Proficient with Python/R and SQL/Bigquery.
- Experience designing, running, and analysing A/B tests (experiments) on a tech consumer product.
- 2-3 years Minimum of experience at a startup, scale-up Crypto company.
- Demonstrated experience of autonomy and agency in overseeing entire projects from identifying problems, testing solutions to communicating results.

- Can work full time in London 5 days a week.

Preferred:
- Experience with crypto and blockchain data.
- Strong data visualisation skills (esp. Graphana)
- Experience with events tracking data (esp. FullStory)
- Strong statistical or causal inference skills.
- Experience building data pipelines (esp. Dataform)
- Experience with Machine Learning methods.

Additional notes for Product Data Scientist:

Autonomous and Self-Motivated: here we want someone who is going to be pro-active (identify and find work) rather than reactive (be assigned work). Either reaching out to other teams to help them with measurement of changes, or who can spot problems/opportunities and drive forward changes needed.

Specialist vs Generalist: ideally here we want someone who isn’t too excessively focused on a specific aspect of Data Science (e.g. only ML experience, only experience with non-experimental methods)

Consumer Product: this means basically any work that was focused on an app or website, rather than more general data science (finance, health, education, economics, etc.). When the data scientist was involved in measuring changes to the app/website.

Overseeing Projects: what we mean here is that the person has experience managing projects directly. From the ideation phase, to prioritisation, stakeholder management, design/analysis, and rollout.

Data Visualisation: this would involve building and automating dashboards (experience with Graphana is a plus here), but also being able to visualise data analysis in a clear, concise, digestible format.

Racking Data: this type of data captures how a user moves throughout (and interacts with) our website: the consumer funnel. What they clicked, what sessions they had, did they place an order, did they experience an error, etc.. Our tracking data comes from FullStory so experience with it is a plus.

Statistical or Causal Inference: these capture non-experimental methods of data analysis. Essentially, can this person give us learnings and insights from non-experimental (observational) data. This work is often the type of thing that starts experimental work: you see something interesting, dig into it and find the relationships, build a hypothesis of what is going on and then run an experiment to test that. It would also include econometric modelling methodologies (using correlations to model the impact of a change).

Compensation & Package

Compensation & Package

• Significant upside

• Competitive salary

• Negotiable equity opportunities