blackon Notebooks, Pandas warts, Multi-task RL and Successful Data Science Projects
Did you know that M&S are hiring for several “Senior & Lead Data Science vacancies”? See the jobs listings below for details.
Last issue I noted that
scikit-learn v1 was coming but I didn’t know how soon - it turns out it is being released in “release candidate” mode right now. On hackernews there’s a discussion about this and the general DS ecosystem in Python which somewhat inevitably devolves into a critique of Pandas. If you’re at all frustrated with Pandas (not sklearn!) you might find the discussion and alternate libraries interesting - both polars (non-Numpy Pandas-like DataFrame library) and proplot (matplotlib sane-defaults wrapper) get useful mentions.
Do you have any critiques of Pandas to share with me? I’m always collecting war stories with a plan to create an intermediate course to make working with Pandas more efficient and easier to understand.
Do you like
black and wish it was easy to run it to tidy your code in a Notebook? Marco shared nbqa a while back which enables that but now…
black knows how to reformat the code in a Notebook natively. Just upgrade to the latest version of
black and you can work directly on your Notebooks.
I’m starting to plan to run a final iteration of my Successful Data Science Projects course for this year around late November - reply if you’d like a notification for the release date for you or a colleague. The goal will be to help you derisk projects, increase the chance of valuable deliverables and to give you some new tools that’ll smooth your work process.
Shagun Sodhani of PyData has shared the following (do you have a library to share?):
“Reinforcement Learning (RL) has led to several groundbreaking innovations, from defeating the world champion of Go to synthesizing molecules and drugs. RL has benefitted from a mature ecosystem of open-source frameworks that have enabled more and more people to use RL for their use cases. A subdomain of RL is multi-task RL, where the agent should perform multiple tasks at once. However, there are far fewer resources for people to start with multi-task RL, and very often, people have to implement all the components from scratch.
The two key components in a multi-task RL codebase are (i) Multi-task RL algorithms and (ii) Multi-task RL environments. FAIR recently open-sourced two libraries for multi-task RL. MTRL provides components to implement multi-task RL algorithms, and MTEnv is a library to interface with existing multi-task RL environments and create new ones.”
In the last issue the top link (14% clicks) was the open source flow forecast for deep learning based time series forecasting - thanks Isaac. Did you get to try it? If so - any joy? Do you have a library or link to share here?
See recent issues of this newsletter for a dive back in time.
About Ian Ozsvald - author of High Performance Python (2nd edition), trainer for Higher Performance Python, Successful Data Science Projects and Software Engineering for Data Scientists, team coach and strategic advisor. I’m also on twitter, LinkedIn and GitHub.
Jobs are provided by readers, if you’re growing your team then reply to this and we can add a relevant job here. This list has 1,400+ subscribers. Your first job listing is free and it’ll go to all 1,400 subscribers 3 times over 6 weeks, subsequent posts are charged.
We are looking for an enthusiastic and talented researcher to help us build the next generation of traffic simulation systems.
You will be researching cutting-edge techniques from the fields of data science, computer science, and software development, and applying them to this domain. Your work will help to make simulation a cost-effective tool which can be used ubiquitously across the mobility ecosystem to solve a broad range of problems in transport planning, scheme design and appraisal, and operational control.
We are hiring a Data Engineer to join our team to help us set up the components of our client’s data platforms (such as data feeds, data warehouses, ETL infrastructure, etc). You’d also design and develop client-specific SQL data models that produce clean, structured and meaningful data sets for the business and other data functions (using dbt). And on top of that, help us build ETL scripts in python to extract data from APIs or perform pipeline transformations.
You’ll work with some of the most ambitious companies in the D2C startup ecosystem, including Pollen, On Deck, Ecosia, PensionBee, and Curio Labs. We offer a competitive salary (this is a junior role, range 45-55k) and a ton of benefits (enhanced parental leave, generous pension scheme, gym membership, refreshment allowance, home office allowance).
Beatchain is a music distribution and social media marketing and analytics platform that works with up-and-coming artists as well as established record labels. We are looking for a data engineer/scientist working in Python to help users manage, understand and put their data into context. Data sources include social media and music platforms scraped over hundreds of thousands of accounts using Scrapy, APIs including over two million Spotify playlists, and large quantities of streaming data from our distribution and record label partners.
This is a junior to mid-level role, you would be working within a small back-end team alongside the lead data-scientist. While the day-to-day ingestion and transformation of data is maintained, we research ways of presenting data to users through visualizations and predictive analytics. Recently, we used graph embeddings to model relationships between artists and genres to recommend related artists for social media campaigns. We use the familiar PyData Python/Pandas/NumPy stack deployed via AWS Lambda, Step Functions and Batch. Data lives in AWS RDS, DynamoDB and Redshift, migrating to Google BigQuery.
Here at Gousto, we are on a mission to become the UK’s favourite way to eat dinner!
We’re hiring for multiple Data Science positions: - Principal Data Scientist - (Menu) https://apply.workable.com/gousto/j/3C7165186A/ - Principal Data Scientist (Supply) https://apply.workable.com/gousto/j/4709837FEC/ - Data Scientist (Growth) https://apply.workable.com/gousto/j/C9F991E124/
If you want to work on some seriously interesting projects and get discounted Gousto boxes as part of the benefits package, please apply using the links above, mentioning that this newsletter sent you there!
See here https://www.gousto.co.uk/jobs for benefits and check out our blog: https://medium.com/gousto-engineering-techbrunch
At Vivacity, we make cities smarter. We gather real-time data from our sensors to reduce congestion, spot dangerous manoeuvres on the road to improve safety, and support autonomous vehicles.
You will join our existing Product team and actively shape the product vision and technical roadmap to ensure we are constantly innovating and meeting our users’ data needs.
Kindred’s ambition is to be the most insight-driven gambling company and in the last few years we’ve invested heavily in our data and analytics capabilities. We are now at the next stage of our journey, embarking on an initiative to enhance our sports and racing modelling and quantitative analysis capabilities.
The Quantitative Team work closely with the existing data science function to play an important role in delivering a truly innovative and unparalleled experience for the customers of our sportsbook brands. This work builds upon a culture of “data as a product” to significantly extend our proof-of-concept efforts in this area.
We are looking for a software engineer with a strong interest in sporting applications and experience in building solutions to handle varied external data sources. On joining, you will be responsible for creating exceptional quality data products, primarily based on sports event and market odds data, for use within the Quantitative Team and the wider business. Your work will be integral in the team’s delivery of market-leading probability and machine learning models to support our commercial and operational functions and decision making processes.
Kindred’s ambition is to be the most insight driven gambling company and in the last few years we’ve invested heavily in our data and analytics capabilities. The quantitative team work closely with the existing data science function to play an important role in delivering a truly innovative and unparalleled experience for the customers of our sportsbook brands. The work will build upon a culture of “data as a product” to significantly extend our proof-of-concept efforts in this area.
We are now looking for a talented Quantitative Analyst to join our team to help shape our sport and racing modelling efforts. This role provides an exciting opportunity to be a pivotal part of the team. On joining, you will be responsible for performing data analysis and building probability and machine learning models to derive descriptive and predictive insight about sporting events. Your work will help to deliver market-leading tools and capabilities to support our commercial and operational functions and decision making processes.
Kindred Group use data to build solutions that deliver our customers the best possible gaming experience and we have ambitious plans to get smarter in how we use our data. As part of these plans we’re looking to recruit a Lead Data Scientist to drive our advanced analytics initiatives and build innovative solutions using the latest techniques and technologies.
• To lead, manage and deliver our advanced analytics initiatives using cutting edge techniques and technologies to deliver our customers the best online gaming experience. • Working in cross functional teams to deliver innovative data driven solutions. • Able to advise on best practises and keep the company abreast of the latest developments in technologies and techniques • Building machine learning frameworks to drive personalisation and recommendations. • Building predictive models to support marketing and KYC initiatives. • Continually improving solutions through fast test and learn cycles • Analysing a wide range of data sources to identify new business value • Be a champion for advanced analytics across the business, educating the business about its capability and helping to identify use cases
Recommenders is Elsevier’s suite of recommendation systems, which uses Data Science and machine learning techniques to keep researchers appraised of developments in their field, new funding opportunities, finding peer reviewers and papers related to their work. We’re looking for a data engineer to help us build the pipelines which extract features from the unparalleled collection of research data flowing through our systems.
You’ll be working in a modern technology stack (AWS, Scala, Spark, Kafka, we’re currently looking at SageMaker and Kedro) as part of a small cross-functional team. If you’re interested in learning more, please contact Stuart White at the email address below.
Climate Policy Radar is a not-for-profit climate AI startup on a mission to map the global policy landscape, harnessing machine learning to create the evidence base for informed decision-making. Our work helps governments, the private sector, researchers and civil society to advance effective climate policies rapidly, replicate successful approaches and avoid failed ones, enhance accountability and promote data democratisation.
We are building the capability to collect and structure climate policy documents from all around the world. Now, at the beginning of this exciting journey, we need an exceptional individual with broad practical experience of ML and NLP to extract information from large and complex unstructured documents. You will need the creativity and passion to write the playbook, and be comfortable working in situations where uncertainty is high, defining the problems as much as the solutions. You will be willing to roll up your sleeves and dive deep into working on a wide range of areas, including the design of data labelling strategies, stakeholder collaboration and model deployment.
Lean provides Payment and Data APIs to unlock the financial technology sector and enable financial innovation in the Middle East.
We launched our first products to market at the beginning of 2021 and now support over 90% of the retail banking market in the UAE. With ambitions to build an entire ecosystem for Fintech in the region we’re now looking to expand to new regions and support stakeholders from end-users, to Fintechs, regulators and financial institutions.
As we collect more raw data and enable an increasing variety of use cases, our data science products and processes will play an important role in Lean’s advancement within the Fintech ecosystem. We are looking for an ML Engineer with a software engineering background and a strong interest in innovative financial applications. Your role will be to extract exciting and scalable features from the river of data that flows through our system.
Here at M&S the data science function builds end-to-end AI and machine learning solutions in retail and e-commerce and helps our colleagues in Food, Clothing & Home, Fashion, Marketing, Loyalty, Supply Chain, Growth, Customer Services etc. driving value from data and create personalised experiences for our customers. We apply state of the art machine learning techniques to solve a variety of problems such as outfit recommendations in fashion, personalised offers for our loyalty program, pricing optimization, demand forecasting for supply chain, product waste management for retail, and AI powered campaigns for our marketing. We are hiring at both Senior and Lead levels. If you would be interested in finding out more, please contact me on the below email address.