How To Hire Data Scientist For Your Company?

A data scientist is someone who makes value out of data. Such a person proactively fetches information from various sources and analyzes it for a better understanding of how the business performs, and to build AI tools that automate certain processes within the company.

There are many definitions of this job, and it is sometimes mixed with the Big Data engineer occupation. A data scientist or engineer may be X% scientist, Y% software engineer, and Z% hacker, which is why the definition of the job becomes convoluted. The actual ratios vary depending on the skills required and type of job. Usually, it’s considered normal to bring people with different sets of skills into the data science team.

Data scientist duties typically include creating various machine learning-based tools or processes within the company, such as recommendation engines or automated lead scoring systems. People within this role should also be able to perform statistical analysis.

In this article, we present a sample data scientist job description, for you to adjust depending on your actual needs to create a perfect job advertisement, and to find the person that will help you get the answers you are looking for.

Data Scientist – Job Description and Template

Company Introduction

{{Write a short and catchy paragraph about your company. Make sure to provide information about the company culture, perks, and benefits. Mention office hours, remote working possibilities, and everything else you think to make your company interesting. Data scientists like to take challenges – anything that shows how the role could make an impact might help attract top talent.}}

Job Description

We are looking for a data scientist that will help us discover the information hidden in vast amounts of data, and help us make smarter decisions to deliver even better products. Your primary focus will be in applying data mining techniques, doing statistical analysis, and building high-quality prediction systems integrated with our products. {{Depending on your needs, you can write very specific requirements here, like: “automate scoring using machine learning techniques”, “build recommendation systems”, “improve and extend the features used by our existing classifier”, “develop internal A/B testing procedures”, “build system for automated fraud detection”, etc.}}

Responsibilities

  • Selecting features, building and optimizing classifiers using machine learning techniques
  • Data mining using state-of-the-art methods
  • Extending the company’s data with third-party sources of information when needed
  • Enhancing data collection procedures to include information that is relevant for building analytic systems
  • Processing, cleansing, and verifying the integrity of data used for analysis
  • Doing ad-hoc analysis and presenting results in a clear manner
  • Creating automated anomaly detection systems and constant tracking of its performance
  • {{Select from the above and add other responsibilities that are relevant}}

Skills and Qualifications

  • Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
  • Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc {{depending on specific project requirements}}. Excellence in at least one of these is highly desirable
  • Great communication skills
  • Experience with data visualization tools, such as D3.js, GGplot, etc.
  • Proficiency in using query languages such as SQL, Hive, Pig {{actual list depends on what you are currently using in your company}}
  • Experience with NoSQL databases, such as MongoDB, Cassandra, HBase {{depending on project needs}}
  • Good applied statistics skills, such as distributions, statistical testing, regression, etc.
  • Good scripting and programming skills {{if you expect that the person in this role will integrate the solution within the base application, list any programming languages and core frameworks currently being used}}
  • Data-oriented personality
  • {{Mention any other technology that such a person is going to commonly work with within the organization}}
  • {{List education level or certification you require}}

What Happens When You Hire a Data Scientist Without a Data Engineer? Guest Post by Vladislav Supalov

Hey Folks,
Vladislav‘s photo
I’m Vladislav! If you care about AI, machine learning, and data science, you should have heard of data engineering. If you haven’t, or would like to learn more – then this is *exactly* for you. Helping companies to make use of their data is a fascinating topic! I’ve spent quite a bit of time building MVP data pipelines and would like to help you avoid one of the worst mistakes you can make when starting out on a serious project.
Having solid data plumbing in place is pretty darn important if you want to work with company data without wasting time and money. The natural train of thoughts when people want to make use of data “the right way”, usually ends at “we should hire a data scientist”.
That’s a mistake in almost every case. You need to take care of data engineering before that. Here are a few of my favourite pieces of writing on the topic:

What Happens When You Hire a Data Scientist Without a Data Engineer

This one is brief but worth a read. The most important points made, is the wasted time and an observed high tendency for a data scientists who are not given the right tools to quit.
A complete story of getting an analytics team up and running within 500px. Samson did a lot of stuff right, which is admirable. Take note of the tech choices, Luigi, in particular, to get data into a data warehouse. A great example of a well-thought-out way to work with data. One of the major mistakes he points out: not putting enough effort into data evangelism.

Your Data Is Your Lifeblood — Set up the Analytics It Deserves

An utterly amazing interview, full of great advice. I especially love that he points out that you should take care of making both event and operational transaction data available. Only if you combine them, you have a complete picture.
A very long interview with the Head of BI at Stylight. Konstantin did an impressive job in his first year and shares a lot of insight. This is not exactly about data engineering but on the topic of giving a company access to data and how to approach it. One of the most important takeaways for me was his advice to secure a small win for as many people as possible in the company when starting out. There are a lot of low-hanging fruits and you get the best ROI and a lot of goodwill from making them available.
Hope you’ll get a lot of value from those articles! If you want to learn more about data engineering, data pipelines and the stuff I do, scroll to the bottom of the last article and subscribe to Beyond Machine and  Vladislav‘s mailing list.

The Secret Behind one of the biggest online marketplace “OLX Group”  How do they utilize their data and machine learning algorithm? 

OLX Group is a global online marketplace operating in 45 countries and is the largest online classified ads company in India, Brazil, Pakistan, Bulgaria, Poland, Portugal, and Ukraine. It was founded by Alec Oxenford and Fabrice Grinda in 2006.

A platform that connects buyers and sellers in more than 40 countries and has hundreds of millions of customers per month faces many challenges that are to some extent similar but also somewhat different to online retail.

There are main 3 challenges: 

Challenge 1: User experience. When the user navigates the platform and what are the recommendations and the results when doing searches, etc.

Challenge 2: Identifying what is that makes some advertisements much more liquid (easy to sell) than others.

Challenge 3: The reminder after purchasing the items. Predicting if an item is sold 15 days after its entry into the system.

 

As part of the solution for a good user navigation and browsing experience, it is useful to have a good estimate if a specific advertisement has been already sold so that we don’t show it again in the recommendation or search output. This is a probabilistic time-series prediction problem. Another important aspect connected to the previous case is identifying what is that makes some advertisements much more liquid (easy to sell) than others. For this particular case, understanding how the model is making decisions is really important as the outcome can be provided to the sellers in order to improve the liquidity of their advertisements. For the reminder of we will focus on this specific liquidity prediction problem, predicting if an item is sold 15 days after its entry in the system, and we will use XGboost and eli5 for modelling and explaining the predictions respectively.

 

 

XGboost is a well known library for “boosting”, the process of iteratively adding models in an ensemble of models that target the remaining error (pseudo-residuals). These “week learners” are simple models and are only good at dealing with specific parts of the problem space on their own, but can significantly reduce bias while controlling variance (giving a good model in the process) due to the iterative fitting approach followed in constructing this type of ensemble. The data we have available for this problem include textual data (the title and textual description of the original advertisement, plus any chat interactions of the seller and potential buyers), as well as categorical and numeric data (the category of the advertisement, the brand and model of the item, the price, number buyers/sellers interactions for each day after the entry, etc.). The data sample we are using here is a relatively small part of the data from some countries and categories only, so in many of its properties it is not representative of the entire item collection. Nevertheless, let’s start with some basic data munging.

 

The histogram of the day that an item was sold is shown above. We can easily see that most items are sold in the first days after the respective advertisement is placed, but there are still significant sales happening a month later as well. With respect to the day an advertisement is added to the platform, we can see that there is a peak on weekends, but other days are roughly at the same level. Finally, with respect to the hour, an advertisement is added to the platform, we can see in the figure below that there is a peak around lunchtime, and the second peak after work hours. One way to capture more complicated relations is to use the pairplot functionality of the seaborn library. In this case we will get the combinations of scatterplots for the selected columns, while in the primary diagonal we can plot something different, like the respective univariate distributions. We can see that the number of buyers interaction in the first day is a strong predictor if an item will be sold early or late. We can also see that category id is very important predictor as well, as some categories in general tend to be much more liquid than others. Now that we are done with the basic data munging we can proceed to make a model, using the XGboost library.

 

Using a hyperparameter optimization framework we can find out the hyperparameters that work best for this data. Since we are interested also on the output confidence of the prediction itself (and not only on the class), it is typically a good idea to use a value for min_child_weight that is equal or larger than 10 (given that we don’t loose in predictive performance) as the probabilities will tend to be more calibrated. The ranking of the features from the XGboost model is shown in the figure above. Although feature ranking from tree ensembles can be biased (favoring for example continuous or categorical features with many levels over binary or categorical features with few levels) and in addition if features are highly correlated the effect can be splitted between them in non-uniform way, this is already a good indication for many purposes. Now we select one specific instance at prediction time. Using eli5 we get an explanation of how this instance was handled internally by the model, together with the most features that where the most important positive and negative influences for this specific sample.

 

As we can see the sample was classified as being liquid, but still there was some pull down from the text properties (title length, title words, etc) which we can use to provide guidance to the seller for improving the advertisement.

How to land the sexiest job- Data Scientist in 2018?

We have conducted some research online that we found out the major obstacles and challenges to work as data scientist or to switch a career to be the data scientist?

#Don’t know where to start or where to learn?

#No Network to find a job or company you like?

#No past experience working as the data scientist? Even you know how to write the algorithm?

Here come the 8 Essential Tips to follow

1. Set your goal first

Looking for new challenging jobs? Just enjoy algorithm or mathematics? Want to start your own startup? Have a special problem, want to look for the solution? Or just to have a better job with more money?

2. Find someone who went through the path that you are interested in, then ask him/her to be a mentor?

It is like running a startup, it takes a lot of energy, time, dedication, motivation, discipline, sometimes even amount of money. You can learn all the best deep learning courses from those famous professors and experts all over the world, however, when it comes to soft skill, you need a mentor to help you, if he/she has even extensive networks, that is even better.

3. Use online education and make a plan to learn and study

Needless to say Andrew Ng’s deep learning courses at Coursera, partnered with NVIDIA Deep Learning Institute.

Andrew Ng is one of the best instructor and teacher I have ever heard and seen, he is perfect articulated, patient, and full of passion to teach as well. I personally took a couple of his classes online at Standford. (the course was really simple enough for anyone who is determined to start!

Btw, I sucked at Linear Algebra during high school 🙁

If you like more entertaining ones, the Youtube star: Siraj Raval also has Youtube channel and Udemy courses online to follow and learn, his goal is to use machine learning to build anything you can be dreamed of. After enough time for studying and online learning, it is always great to apply to the topic you are especially interested.

Don’t forget the traditional way, reading a book: The Bible of the deep learning which was written by Ian Goodfellow and Yoshua Bengio and Aaron Courville.

Certainly, if you like more personal touch, we offer Deep Learning Bootcampin January in Berlin with the topic of NLP, time series forecasting, and computer vision. Our instructors are from IBM and the famous research institute Max Planck Institute. Romeo Kienzler, he also has Youtube channel and is one of the most successful Deep Learning instructor in Europe.

4. Join some online communities and also offline to help you to learn and engage also receiving some feedback to improve

Online: GithubCodeacademyKaggleKD Nuggets

Offline: MeetupEventbrite

5. Choose a Tool / Language and stick to it

This is one of the most common questions for beginners. We can have one more article to go deeper into that in the future. For now, choose a language that you are the most familiar with or the simplest for you. If you are completely new, then choose in between the data handling capabilities, advancements in tool, career chance, deep learning support. From my personal experience, I was consulting a machine learning startup with one Python scientist, one R scientist, every time when I had problem with data, I asked Python scientist to write me a quick code to fix my problem while R could not really deal in a such an easy way. Python becomes love of my life for now.

6. Focus on real cases and applications not just learning theory

Think about some scenario you want to work on, music recommendation, Game of throne’s ending? Parking problem? Predict stock market? (Check out our DeepLearning Bootcamp on Eventbrite)

7. Don’t forget machine learning and deep learning it comes down to your mathematics capacity.

Warning: do not just take deep learning and NLP courses, but forget math is the real foundation. Take also some linear algebra courses also or at least linear algebra.

8. Finally, you can start the network but always remember, play smart.

Don’t waste too much energy and time on it, especially a lot of them come with free alcohols…Check the topic before you attend also attendees, are they your target audiences or not really? Or sign up some recruiting event, there will be tons of recruiters and HR managers from companies. (We are launching our First Recruiting Day focused on the data team recruitment in March 2018)

P.S. M.I.E is currently re-branding to Beyond Machine, our mission to connect and boost AI ecosystem through connecting and training. We will launch series events in 2018, following with Deep Learning Bootcamp, Data Analytics Workshop, Recruitment Day, Self-Driving Workshop, Machine Learning Week. Furthermore, we will launch also outside of Berlin and bring more corporation and partnership exchange in and outside of Germany. If you are interested in any kind of sponsorship, partnership, just simply drop us a line.

 

Build it, Train it, Test it!