✨✨ BIG NEWS: We are hiring!! ✨✨
Amazing Research Software Engineer / Research Data Scientist positions within the @turinghut23 group at the @turinginst, at Standard (permanent) and Junior levels 🤩

👇 Here below a thread on who we are and what we

We are a highly diverse and interdisciplinary group of around 30 research software engineers and data scientists 😎💻 👉 https://t.co/KcSVMb89yx #RSEng
We value expertise across many domains - members of our group have backgrounds in psychology, mathematics, digital humanities, biology, astrophysics and many other areas 🧬📖🧪📈🗺️⚕️🪐
/ @DavidBeavan @LivingwMachines
In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩‍💻 presentation for some examples: https://t.co/GzO9m1CGz4
We create software packages to analyse data in a readable, reliable and reproducible fashion and contribute to the #opensource community, as @drsarahlgibson highlights in her contributions to @mybinderteam and @turingway: https://t.co/pRqXtFpYXq #ResearchSoftwareHour
To know more, here's a day in the life of a Research Data Scientist in our group, @louise_a_bowler ! 💯👩‍💻
We are expert collaborators and work with the @turinginst community and beyond to enhance the applicability of their research. Check out the spotlights of some of our members 🙌:
https://t.co/KknkBtl6bg / @CamilaRangelS @radka_jersak @louise_a_bowler
We often host and work together with members of the civil service @faststreamuk program - see @Kevinzhangxu's thread about his experience with us earlier this year 👌: https://t.co/0UeLNhIJ9L
We highly value the development and career progression of our members: we have a dedicated Junior training role, and through our internal promotion scheme people have been promoted to Senior, Principal and even to Director! If you'd like to know more, contact @martinoreilly!
Some of our projects are covered in the Turing Podcast 🎙️, started by @EChalstrey, a (former Junior!) Research Data Scientist from the team: https://t.co/kmpPE1UldD
We work on topics ranging from data security 🔐 to air traffic control ✈️, from modelling contact-tracing during the current pandemic 😷 to the industrial revolution 🚂 (@LivingwMachines): to find out more, see our website! 👇
Or check out our brand new @TuringDStories project!! 😻😻https://t.co/PEWnUMxoXc / @DavidBeavan @CamilaRangelS @Kevinzhangxu
And now, about you...
If you understand the importance of good practices for producing reliable software and reproducible analyses (as described for instance by @turingway), are fluent in a programming language used in #DataScience, and love to learn new skills, we strongly encourage you to apply!
In addition, if you feel that your profile would support the Group's activities, we would love to hear from you! Look at our historian turned into data scientist 👨‍💻: https://t.co/WJyc77kt49 / @f_nanni @LivingwMachines
Or at the contributions of our seismologist @kasra_hosseini to research in #digitalhumanities
Experience in teaching and training, building #opensource communities, scientific computing and other themes are also very welcome - surprise us!
In particular, if you have worked (or would love to work!) on cloud ☁️ or high performance computing get in touch! Tomas is available if you'd like to know more 😉:
https://t.co/dgkK45JzN6 #HPC
We welcome any informal inquiries and will set up drop-in sessions if you'd like to have a chat with members of the group before applying. Just send a message to @f_nanni or @CamilaRangelS, or subscribe here:
📨 https://t.co/3e4b0dsEDJ
Again, consider applying or pass it on to colleagues and friends who might be interested!!
👉 https://t.co/LnGW7JNQX5

More from Data science

I have always emphasized on the importance of mathematics in machine learning.

Here is a compilation of resources (books, videos & papers) to get you going.

(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)

📘 Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong


Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.

📘 Pattern Recognition and Machine Learning

by Christopher Bishop

Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.

📘 The Elements of Statistical Learning

by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...

📘 Probability Theory: The Logic of Science

by E. T. Jaynes

Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇

https://t.co/EwwOzgfDca : Deep Learning framework in Java that supports the whole cycle: from data loading and preprocessing to building and tuning a variety deep learning networks.

https://t.co/J4qMzPAZ6u Framework for defining machine learning models, including feature generation and transformations, as directed acyclic graphs (DAGs).

https://t.co/9IgKkSxPCq a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.

https://t.co/EAqn2YngIE : TensorFlow Java API (experimental)

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I like this heuristic, and have a few which are similar in intent to it:

Hiring efficiency:

How long does it take, measured from initial expression of interest through offer of employment signed, for a typical candidate cold inbounding to the company?

What is the *theoretical minimum* for *any* candidate?

How long does it take, as a developer newly hired at the company:

* To get a fully credentialed machine issued to you
* To get a fully functional development environment on that machine which could push code to production immediately
* To solo ship one material quanta of work

How long does it take, from first idea floated to "It's on the Internet", to create a piece of marketing collateral.

(For bonus points: break down by ambitiousness / form factor.)

How many people have to say yes to do something which is clearly worth doing which costs $5,000 / $15,000 / $250,000 and has never been done before.