How To Attract Data Scientists To Your Business (And How Not To)

Wesley Taupin our consultant managing the role
Posting date: 4/11/2019 8:15 AM
Whilst the role of Data Scientist is still considered one of the most desirable around, many businesses are finding that a shortage of strong, experienced talent is preventing them from growing their teams sufficiently. With a huge demand for such a small talent base, enterprises have begun to ask what they can do to ensure that they can secure the skillsets they need. 

If you’re looking at hiring a Data Scientist, there are a few key Do’s and Don’ts that you need to bear in mind:

THE DO’S


Create A Clear Career Path

In most companies, a career path is defined. Usually you grow from junior to senior to manager etc. However, Data Scientists often like to become experts rather than moving up the traditional career ladder into people management roles. And, once a Data Scientists becomes an expert, they want to remain an expert. To do this, they need to keep up with the latest tools and data systems and continually improve. That’s why it’s important that you put in place a clear career path that suits the Data Scientists. In addition to the possibility of leading teams on projects, businesses should provide opportunities for financial progression that reflect growing skillsets in addition to increased responsibilities. 


Let Them Be Inventive 

One of the biggest turn-offs for Data Scientists is lack of opportunities to try new techniques and technologies. Data Scientists can get bored easily if their tasks are not challenging enough. They want to work on a company’s most important and challenging functions and feel as though they are making an impact. If they are asked to spend their time on performing the same tasks all the time, they often feel under-utilised. Providing forward-looking projects, with innovative technologies, gives Data Scientists the opportunity to reinvent the way the company benefits from their Data.

Provide Opportunities To Discover 

As part of their attitude of constant improvement, Data Scientists often feel that attending conferences or meet-ups helps them become better at their role. Not only are these a chance for them to meet with their peers and exchange their Data Science knowledge, they can also discover new algorithms and methodologies that could be of benefit to your business. Businesses that allow the time and budget for their team to attend these are seen as much more attractive prospects for potential employees in a competitive market. 


Give them the freedom they need

Data Scientists are efficient workers who can both collaborate and work independently. Because of this, they expect their employers to trust that they will get the job done without feeling micro-managed. By offering flexible working, be it flexi-hours or working from home options, enterprises can make themselves a much more appealing place to work. 

THE DON’TS


Hire The Wrong Skillset

As many companies begin to introduce Data teams into their business, they can often attempt to hire for the wrong job. Generally, this will be because they automatically jump to wanting to hire a Data Scientist, but actually need a different role placed first. For example; a company may be looking to hire a Machine Learning specialist, but their data pipeline hasn't even been built yet. There are many talented candidates out there who want to work with the latest technology and solve problems in complex ways. But the reality is that a lot of businesses aren’t ready for their capabilities yet. Before hiring, asses what skillsets you really need and be specific in your search. 


Undervalue Their Capabilities 

There are still a large number of organisations that do not value Data within their culture and Data professionals pick up on this incredibly quickly. If they feel that their work is under appreciated, and they know that there is high demand for what they do, they will not waste their time sticking around. Ask yourself how you see your Data team contributing to the company as a whole and make this clear within your organisation. Advanced Data Scientists don't want to work on dashboarding so make sure that their work will have an impact and explain how you see this happening during the interview process. Additionally, be aware of other financial implications that their hire may have. It’s likely that they’ll need a supporting Data Engineer to work with and, if they don’t have access to one, they have another reason to look elsewhere. 

The Data Scientist market is a candidate-driven one and, as a result of this, businesses need to go the extra mile to ensure they get the best talent around. By offering a strong set of benefits, the opportunity to grow and progress, and an environment that values Data, enterprises can stand out amongst the crowd and attract the best Data Scientists on the market. 

If you’re looking for support with your Data Science hiring process, get in touch with one of our expert consultants who will be able to advise you on the best way forward. 

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Data Science Interview Questions: What The Experts Say

Our friends at Data Science Dojo have compiled a list of 101 actual Data Science interview questions that have been asked between 2016-2019 at some of the largest recruiters in the Data Science industry – Amazon, Microsoft, Facebook, Google, Netflix, Expedia, etc.  Data Science is an interdisciplinary field and sits at the intersection of computer science, statistics/mathematics, and domain knowledge. To be able to perform well, one needs to have a good foundation in not one but multiple fields, and it reflects in the interview. They've divided the questions into six categories:  Machine LearningData AnalysisStatistics, Probability, and MathematicsProgrammingSQLExperiential/Behavioural Questions Once you've gone through all the questions, you should have a good understanding of how well you're prepared for your next Data Science interview. Machine Learning As one will expect, Data Science interviews focus heavily on questions that help the company test your concepts, applications, and experience on machine learning. Each question included in this category has been recently asked in one or more actual Data Science interviews at companies such as Amazon, Google, Microsoft, etc. These questions will give you a good sense of what sub-topics appear more often than others. You should also pay close attention to the way these questions are phrased in an interview.  Explain Logistic Regression and its assumptions.Explain Linear Regression and its assumptions.How do you split your data between training and validation?Describe Binary Classification.Explain the working of decision trees.What are different metrics to classify a dataset?What's the role of a cost function?What's the difference between convex and non-convex cost function?Why is it important to know bias-variance trade off while modeling?Why is regularisation used in machine learning models? What are the differences between L1 and L2 regularisation?What's the problem of exploding gradients in machine learning?Is it necessary to use activation functions in neural networks?In what aspects is a box plot different from a histogram?What is cross validation? Why is it used?Can you explain the concept of false positive and false negative?Explain how SVM works.While working at Facebook, you're asked to implement some new features. What type of experiment would you run to implement these features?What techniques can be used to evaluate a Machine Learning model?Why is overfitting a problem in machine learning models? What steps can you take to avoid it?Describe a way to detect anomalies in a given dataset.What are the Naive Bayes fundamentals?What is AUC - ROC Curve?What is K-means?How does the Gradient Boosting algorithm work?Explain advantages and drawbacks of Support Vector Machines (SVM).What is the difference between bagging and boosting?Before building any model, why do we need the feature selection/engineering step?How to deal with unbalanced binary classification?What is the ROC curve and the meaning of sensitivity, specificity, confusion matrix?Why is dimensionality reduction important?What are hyperparameters, how to tune them, how to test and know if they worked for the particular problem?How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession, and gender? Define a machine learning algorithm for this.How will you inspect missing data and when are they important for your analysis?How will you design the heatmap for Uber drivers to provide recommendation on where to wait for passengers? How would you approach this?What are time series forecasting techniques?How does a logistic regression model know what the coefficients are?Explain Principle Component Analysis (PCA) and it's assumptions.Formulate Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) techniques.What are neural networks used for?40. Why is gradient checking important?Is random weight assignment better than assigning same weights to the units in the hidden layer?How to find the F1 score after a model is trained?How many topic modeling techniques do you know of? Explain them briefly.How does a neural network with one layer and one input and output compare to a logistic regression?Why Rectified Linear Unit/ReLU is a good activation function?When using the Gaussian mixture model, how do you know it's applicable?If a Product Manager says that they want to double the number of ads in Facebook's Newsfeed, how would you figure out if this is a good idea or not?What do you know about LSTM?Explain the difference between generative and discriminative algorithms.Can you explain what MapReduce is and how it works? If the model isn't perfect, how would you like to select the threshold so that the model outputs 1 or 0 for label?Are boosting algorithms better than decision trees? If yes, why?What do you think are the important factors in the algorithm Uber uses to assign rides to drivers?How does speech synthesis works? Data Analysis Machine Learning concepts are not the only area in which you'll be tested in the interview. Data pre-processing and data exploration are other areas where you can always expect a few questions. We're grouping all such questions under this category. Data Analysis is the process of evaluating data using analytical and statistical tools to discover useful insights. Once again, all these questions have been recently asked in one or more actual Data Science interviews at the companies listed above.   What are the core steps of the data analysis process?How do you detect if a new observation is an outlier?Facebook wants to analyse why the "likes per user and minutes spent on a platform are increasing, but total number of users are decreasing". How can they do that?If you have a chance to add something to Facebook then how would you measure its success?If you are working at Facebook and you want to detect bogus/fake accounts. How will you go about that?What are anomaly detection methods?How do you solve for multicollinearity?How to optimise marketing spend between various marketing channels?What metrics would you use to track whether Uber's strategy of using paid advertising to acquire customers works?What are the core steps for data preprocessing before applying machine learning algorithms?How do you inspect missing data?How does caching work and how do you use it in Data Science? Statistics, Probability and Mathematics As we've already mentioned, Data Science builds its foundation on statistics and probability concepts. Having a strong foundation in statistics and probability concepts is a requirement for Data Science, and these topics are always brought up in data science interviews. Here is a list of statistics and probability questions that have been asked in actual Data Science interviews. How would you select a representative sample of search queries from 5 million queries?Discuss how to randomly select a sample from a product user population.What is the importance of Markov Chains in Data Science?How do you prove that males are on average taller than females by knowing just gender or height.What is the difference between Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP)?What does P-Value mean?Define Central Limit Theorem (CLT) and it's application?There are six marbles in a bag, one is white. You reach in the bag 100 times. After drawing a marble, it is placed back in the bag. What is the probability of drawing the white marble at least once?Explain Euclidean distance.Define variance.How will you cut a circular cake into eight equal pieces?What is the law of large numbers?How do you weigh nine marbles three times on a balance scale to select the heaviest one?You call three random friends who live in Seattle and ask each independently if it's raining. Each of your friends has a 2/3 chance of telling you the truth and a 1/3 chance of lying. All three say "yes". What's the probability it's actually raining? Explain a probability distribution that is not normal and how to apply that?You have two dice. What is the probability of getting at least one four? Also find out the probability of getting at least one four if you have n dice.Draw the curve log(x+10) Programming When you appear for a data science interview your interviewers are not expecting you to come up with a highly efficient code that takes the lowest resources on computer hardware and executes it quickly. However, they do expect you to be able to use R, Python, or SQL programming languages so that you can access the data sources and at least build prototypes for solutions. You should expect a few programming/coding questions in your data science interviews. You interviewer might want you to write a short piece of code on a whiteboard to assess how comfortable you are with coding, as well as get a feel for how many lines of codes you typically write in a given week.  Here are some programming and coding questions that companies like Amazon, Google, and Microsoft have asked in their Data Science interviews.  Write a function to check whether a particular word is a palindrome or not.Write a program to generate Fibonacci sequence.Explain about string parsing in R languageWrite a sorting algorithm for a numerical dataset in Python.Coding test: moving average Input 10, 20, 30, 10, ... Output: 10, 15, 20, 17.5, ...Write a Python code to return the count of words in a stringHow do you find percentile? Write the code for itWhat is the difference between - (i) Stack and Queue and (ii) Linked list and Array? Structured Query Language (SQL) Real-world data is stored in databases and it ‘travels’ via queries. If there's one language a Data Science professional must know, it's SQL - or “Structured Query Language”. SQL is widely used across all job roles in Data Science and is often a ‘deal-breaker’. SQL questions are placed early on in the hiring process and used for screening. Here are some SQL questions that top companies have asked in their Data Science interviews.  How would you handle NULLs when querying a data set?How will you explain JOIN function in SQL in the simplest possible way?Select all customers who purchased at least two items on two separate days from Amazon.What is the difference between DDL, DML, and DCL?96. Why is Database Normalisation Important?What is the difference between clustered and non-clustered index? Situational/Behavioural Questions Capabilities don’t necessarily guarantee performance. It's for this reason employers ask you situational or behavioural questions in order to assess how you would perform in a given situation. In some cases, a situational or behavioural question would force you to reflect on how you behaved and performed in a past situation. A situational question can help interviewers in assessing your role in a project you might have included in your resume, can reveal whether or not you're a team player, or how you deal with pressure and failure. Situational questions are no less important than any of the technical questions, and it will always help to do some homework beforehand. Recall your experience and be prepared!  Here are some situational/behavioural questions that large tech companies typically ask:    What was the most challenging project you have worked on so far? Can you explain your learning outcomes?According to your judgement, does Data Science differ from Machine Learning?If you're faced with Selection Bias, how will you avoid it?How would you describe Data Science to a Business Executive? If you're looking for new Data Science role, you can find our latest opportunities here.  This article was written by Tooba Mukhtar and Rahim Rasool for Data Science Jojo. It has been republished with permission. You can view the original article, which includes answers to the above questions here. 

Hunted’s Ben & Danni On Cybersecurity

Hunted’s Ben & Danni On Cybersecurity

Ben Owen and Danni Brooke are the Co-Directors for the EMEA Practice at Fortalice Solutions, a leading global cyber security and intelligence operations company.  They travel globally to assist clients with their cyber security requirements, bespoke training needs, intelligence and investigations both online and physical and counter fraud training/consultation. They deliver and manage a portfolio of pro-active intelligence solutions to keep people, nations and businesses safe from threats and head up the EMEA operations.  Ben and Danni also feature on the hit Channel 4 show, Hunted and Celebrity Hunted which has been airing for over four years with another series set to be filmed this summer. I caught up with them recently to discuss the latest Fraud, tools and challenges for the Cybersecurity industry. Cybersecurity is an ever-changing landscape. What trends do you anticipate for the next 12 months and beyond? It is always difficult to pin down what the next real trend is going to be in the Cybersecurity space as adversaries are becoming ever more sophisticated.  What was once a very difficult process for skilled individuals is becoming more readily available to novices with advances in software, particularly those shared on the Dark Web. What is an inevitable threat trend in the next 12-months and beyond is the exponential rise in the Internet of Things (IoT).  With a world where everything is hooked up to the web, it is apparent that tech companies selling these devices are under immense pressure to get products to market. The need for speed could mean that some security principles and best practices may be overlooked.   As the UK encountered during the Mirai Botnet attack of 2016, a network of electronic devices acting in concert can cripple the internet or, worst case, become a weapon that could cause actual physical damage as well as cyber damage, power stations, hospital networks to name but a few.   How have Data & Analytics impacted the detection, and prevention, of cyber-crime? A company will have to protect themselves against an enormous amount of cyber threats every second.  A cyber-criminal will only need one successful attempt. Data & Analytics are proving successful in the fight against cyber-crime and their proactive and holistic approach is at keeping people and businesses safe.  Of course, it is Data that is being stolen, but very often Data can come to the rescue.  It helps in a number of ways, e.g. identifying anomalies in employee and contractor computer usage and patterns, detecting irregularities in networks, identifies irregularities in device behaviour (a huge advantage with the rise of the IoT). What one must remember, however, is the people behind the Data.  You can’t simply collect Data and assume you will be able to detect and respond with the right actions.  You need the people with the right analytical skills to sift through the Data, find the right signals and then react to the threat with an appropriate and timely response.   What tools and technologies do you think will become increasingly important in the fraud and cyber-crime landscape? Here at Fortalice we are investing a lot of time into coverage of the Dark Web.  We live in a rapidly changing digital landscape. Criminals, fraudsters, and others are now operating with more sophistication and anonymity. Where do they go to exchange fraudulent details and ideas about current victims? What medium do they use to discuss organisational targets or new ways of defrauding companies? The answer is the Dark Web.  Traditionally, companies fight fraud from the inside out. We want to change this landscape by accessing the entirety of the Dark Web, its pages, shady storefronts, and treasure troves of Data, and drawing on monitoring toolsets to give our clients a 360-degree resource for identifying adversarial communications and movements. It’s all about Internet coverage.  Wherever it is difficult to find – that’s where your threat will be.   A final point to this question is one of sharing tools and techniques.  A collaborative approach is always a good way of making sure the wider audience benefits.  We always work with our clients and offer other services and support outside of our remit to make sure they’re fully protected from a cyber and physical space.   What are the biggest security threats for businesses? Security is fundamentally broken because the design of many security solutions does not design for the human psyche.  Security solutions are bolted on, clunky, and hard to use but because security teams prioritise defending against easier cyber threats, they often don’t focus on the hardware side. The biggest risk to companies and individuals is always defined by the Data that is most important to you or to the business.  For individuals, this might be privacy or identity. For businesses, this could be customer Data, intellectual property, and the company’s money in the bank. The reality is that business executives can’t outspend the (cybersecurity) issue and they must be prepared. Cybersecurity no longer exists in a vacuum and it must be elevated to the conversations held in the boardroom and with senior leadership as well as entire divisions, departments, and organisations. For someone trying to get into security analytics, what skills do you think are key to being successful in the industry? The detail is in the name of the role.  A huge ability to interpret large amounts of technical Data is key to the role, as well as being able to assimilate what it means and how to action it.  Risk management is also key to this role.  Very often you will identify potential risks and you will have to triage those priorities on your own as co-workers won’t have the technical expertise to assist.  You will need to be able to communicate successfully to all levels of a workforce and last but by no means least – a good sense of humour!  When you think you have gotten to understand a new threat or vulnerability a new one will replace it within seconds.  Time to put the kettle on, smile, and get back to work with your analytical prowess.   Within fraud, it's well known that criminals are sharing their approaches, is this mirrored in cyber-security and if so, how is the industry combating this? Criminal collaboration is huge on the web.  First of all, there is no talent shortage for fraud rings or cybercriminals. There are no requirements for fancy university degrees or certifications and the crime ring pays for performance.  They don’t care what you look like, how you dress, or if you clock in during normal work hours. They care about getting the job done - hacking into and stealing information from others. Together they are sadly stronger and more effective.  On Dark Web forums, you will see fraudsters sharing and selling their ‘IP’ knowing that others will also contribute. That way they are all winners.  In the private world ideas equal money. That is of course not a bad thing for business, but it is bad for collaboration. Businesses generally don’t like to share ideas with one another because it has taken them lots of time and expense to get to their product or solution. As cliché as this comment sounds - we have to change this landscape for the greater good.  There are lots of smart government initiatives for national defences in cyber security and fighting high-end cyber-crime but seldom does this have a positive impact locally with smaller businesses.  There is a huge amount of information out there for individuals and advice, but we need to bridge the gap still between criminal collaboration and that of the good guys. If you could change one thing in the industry, what would it be? The mind set of security professionals that humans are the weakest link. We’re not! Humans are at risk because technology is by design, open.  I’d also change the mind set of those not in the Cyber Security industry.  All too often the severity of what is being reported is not taken seriously, nor are budgets set aside for cyber security issues.  That said, it is improving but there is a long way to go.  Ben and Danni spoke to Senior Consultant, Rosalind Madge. Get in touch with Rosalind or take a look at our latest job opportunities here.

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