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Chris Conlan

Data Scientist | Data Engineer | AWS

Can work in or around London

  • 51.509648
  • -0.099076
  • Suggested rate £214 / day
  • Experience 7+ years
Propose a project The project will begin once you accept Chris's quote.

This freelancer is available part-time (2 days per week) but hasn't confirmed their availability in over 7 days.

Part-time, 2 days per week

Propose a project The project will begin once you accept Chris's quote.

Location and workplace preferences

London, England, United Kingdom
Can work onsite in your office in
  • London and around (up to 50km)


Project length
  • ≤ 1 week
  • ≤ 1 month
  • Between 1-3 months
  • Between 3-6 months
Business sector
  • Architecture & Urban Planning
  • Automobile
  • Banking & Insurance
  • Civic & Social Organizations
  • Consulting & Audits
+19 other
Company size
  • 1 person
  • 2-10 people
  • 11 - 49 people
  • 50 - 249 people
  • 250 - 999 people
+2 other


Freelancer code of conduct signed
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Verified email



  • English

    Native or bilingual


Skills (20)

Chris in a few words

- 10 + year using data science, engineering and analytics to solve real-world problems in tech, finance and academia
- PhD in Comuter Science, MSc in Data Science
- Experienced in developing and deploying deep learning models (e.g., feed forward neural networks, graph neural networks, forecasting models etc) on cloud computing architecture
- Skilled in working with big data, geospatial data, timeseries data, relational databases and more
- Highly skilled in Python (numpy, pandas, GeoPandas, PyTorch, tensorflow, networkx), SQL, GIS, GitHub, unix

I am a qualified and experienced data scientist / data engineer. I am technically minded, but also highly personable, easy to get along with and a good communicator. I am open and honest in my working relationship and show respect to others. I can work independently and apply common and commercial sense to the work I am carrying out.


University of Warwick


PHD Computer Science

Coventry, UK

October 2018 - Today (4 years and 6 months)

- Developed deep learning models (e.g., feed forward neural networks, graph neural networks) using PyTorch which learn complex spatio-temporal patterns in cities.

- Worked in an agile way with data engineers at Transport for West Midlands to build a cloud-based (AWS) MLOps platform to perform real-time forecasting of road networks using state of the art graph neural networks.

- Built end-to-end data processing pipelines using Python and SQL which ingest vast and messy spatio-temporal datasets and efficiently generates feature vectors.

- Assisted in West Midlands pandemic planning to help optimally locate COVID-19 vaccinations centres for the region's most clinically vulnerable.

- Lead a development team to build a web based urban analytics tool which is in use by Transport for West Midlands. The tool was built using Python, SQL, vue, and the codebase released on GitHub.

- Enhanced state of the art graph neural networks with novel data types and demonstrated their improved performance against baselines.

- Collaborated with medical school to use the methods I had developed on their datasets to analyse how people access healthcare in low and middle income countries.


Banking & Insurance

Customer Targeting Manager

London, UK

December 2014 - December 2018 (4 years)

Using SQL, SAS and Unix I was responsible for building data selection processes which drove multi-channel marketing and regulatory communications, ensuring the validity of A/B testing and providing reporting on commercial outcomes.

Major Achievements:

- Developed a targeting model in collaboration with commercial teams for a multi-channel marketing campaign in support of the launch of new product, which drove a 160\% increase in sales and 125\% increase in direct marketing response.

- Managed the transition to a new data platform for open market communications which represented significant cost savings to the business, without any negative impact on the rate and success of marketing activity.

- Automated several processes within the department, such as t-testing control segments, fraud detection tools and controls on marketing briefs.

- Managed a team of two people, ensured their development and quality of work.


Digital & IT

Internship in Software Design and Development

October 2013 - December 2013 (2 months)

Developed new app features using Java for commercial-grade mobile apps.


Banking & Insurance

Spend Analyst  - As a freelancer

November 2012 - September 2013 (10 months)

- Developed strategies to drive spend on American Express products, and analysed their effectiveness.
- Implemented statistical techniques on big data using SAS and SQL.
- Developed a full customer segmentation giving an overview of spend patterns across the customer base which assisted the launch of a new product.


Banking & Insurance

Customer Targeting Manager

April 2010 - July 2012 (2 years and 3 months)

- Designing bespoke SAS and SQL programs to perform data selections as per marketing briefs.
- Using SAS and SQL daily to provide analysis on key value drivers for existing Barclaycard campaigns, and scoping for prospective campaigns.
- Advising marketing executives on issues ranging from data functionality and regulatory compliance, to commercial practicality.
- Management; set development goals, appraisals, ensuring quality of work.
- Developing best practice; including implementation of coding standards and guidelines on locking down BAU codes to un-validated changes. Key Achievements
- Achieving success in a short time during a six month secondment. Working with new data in a SAS role, a key deliverable was to design and deliver marketing reports on new business areas.
- Implementing the New Customer Engagement programme. A sophisticated 20 cell, multi-channel campaign. Included therein were regular reports to marketing executives.
- Managing a change of data supplier and selection tool for recruitment. This required close management to replicate historical reports and data selections, while continuing to deliver existing campaigns to hit tight recruitment targets.