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"My experience with Harnham was very positive. Although I was new to the UK, they studied my CV and understood my background, so very quickly they were able to find roles that suited my previous experience. They helped me prepare thoroughly for all of my interviews and gave me prompt and clear interview feedback from their clients.
Throughout the process they also helped me when it came to managing my own expectations. I would sincerely recommend Harnham to my friends and colleagues and I will not hesitate to contact them in the future if I think about changing jobs again."
"I was approached by Harnham who quickly contacted prospective companies on my behalf. It was clear when I worked with them that they were well respected amongst their clients and this reflected well on my CV. Harnham kept in regular contact and provided useful feedback on the interviews I had. I have already recommended them to friends in the industry."
"My experience with Harnham was everything a candidate should expect from a recruitment company. They are the only agency that I would have no hesitation in recommending."
"We have developed an excellent working relationship with Harnham. They understand the needs of our business and consistently deliver a high quality service. Harnham adopt a professional and honest approach with regards to the candidates they put forward, and they give excellent feedback on the market conditions which enables us to remain a competitive and attractive employer."
“We have worked with Harnham for three years now, sourcing candidates for business critical analytics roles. They are a skilled, professional and dedicated team and the ease with which Harnham consultants interpret our requirements and match them to high-calibre professionals means we have consistently been able to capture the best people in the market. Harnham is always our first point of call for ensuring successful analytical placements.”
"The most important aspects of an agency relationship for us are delivery and professionalism. We have found Harnham to score very highly on both of these as well as providing helpful honest feedback on the state of the market and their candidates capabilities."
“Harnham has assisted us over the past 10 months to help expand our customer analytics function. They are a professional, committed and knowledgeable team who have provided high quality candidates and have understood our requirements thoroughly. They have consistently delivered high-calibre professionals for our team and will be an ongoing source for analytical placements.”
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With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our Blogs & News portal or check out our recent posts below.
12. September 2019
The Web Analytics world is evolving. What used to require an understanding of Google Analytics, some tag management and visualisation for presentation purposes has grown into something much more. Whereas Web Analysts may have once been lone players in a Marketing team, they’re now expected to sit as part of, and feed into, an enterprise’s Insight team. This exposure to more comprehensive forms of Data Analysis has led many Web Analysts to explore what the next step in their career could be. Namely, should they move into a Data Science position? For those who are looking to make this move, here are some considerations: Technicalities and Technologies Digital Analytics are not excluded from the debate over what it means to be a Data Scientist, especially given that some with a Data Scientist job title may in fact be Web Analysts, and vice versa. Many Web Analysts are now working with a number of Data Science tools, including SQL, Python, and R. By using these alongside Google or Adobe Analytics, they are able to form a comprehensive view of the customer, using different types of Data, in different forms, from different sources. However, there remains a gap between the use of these tools and actually working within Data Science. The most logical leap for a Web Analyst to make is to a Customer Insight or Digital Insight role. This type of role would still involve the analysis of online Data, but would likely be paired with building models, Predictive Analysis, reviewing customer LTV and creating a picture of customer online, offline and post-purchase behaviour to enable better targeting and retargeting. However, the knowledge gap between Web Analytics and Data Science may be more significant than one would anticipate. Your Current Position As a Web Analyst, you may well sit within a larger Data, Digital or Customer/Marketing Analytics department. Your exposure to these experts is one of the best assets you have available. Use the environment you are in to learn, upskill and gain hands-on experience. Knowledge of the necessary tools and languages is unlikely to be enough to lead to a move into Data Science and by getting hands-on commercial experience, you drastically increase your chances of success. If you are able to expand on the tech that you have already used, take advantage of this. Even if this is just in a consulting capacity, your ability to demonstrate a real-world application of your knowledge makes you significantly more appealing as a candidate. Plus, your knowledge of, and approach to, Web Analytics may actually work to your advantage when it comes to assessing Data quality. Consultancies and agencies often provide the best training opportunities and are more likely to allow you the opportunities to hone new skills. If you are fortunate enough to work in an environment like this, make the most of it. Attitude Is Everything It may sound like a cliché, but Hiring Managers are on the lookout for people that they know will benefit their business and attitude plays a huge part in this. Do not underestimate the importance that is placed on cultural fit during an interview process. Whether you are looking to make a move internally or externally, you should demonstrate your intrigue and willingness to learn. If you already have a strong record of progression within your current career, this will benefit you moving forward. When it comes to preparing, take time to dive into the world of Data Science, attend events and meet-ups, and continue to widen your remit. If you don’t have exposure to Data Science at work then you will also need to be learning SQL, Python and R at home to ensure you have a firm understanding of all the relevant technologies. Whatever role you are looking for, the worst thing you can do is not apply. One of the most common mistakes we see is analysts not applying to an opportunity because they would need to develop in some areas once in the role. If you are able to demonstrate the above attributes many enterprises, particularly agencies and consultancies, may still be willing to take you on. And, if you’re not looking to make a move, don’t panic; Web Analytics skillsets remain highly sought-after and valuable. Whether you’re looking for a new career in Data Science or your next role in Web Analytics, we may have a job for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.
05. September 2019
As Big Data can reveal patterns, trends and associations relating to human behaviour and interactions, it’s no surprise that Data & Analytics are changing the way that the supply chain sector operates today. From informing and predicting buying trends to streamlining order processing and logistics, technological innovations are impacting the industry, boosting efficiency and improving supply chain management. Analysing behavioural patterns Using pattern recognition systems, Artificial Intelligence is able to analyse Big Data. During this process, Artificial Intelligence defines and identifies external influences which may affect the process of operations (such as customer purchasing choices) using Machine Learning algorithms. From the Data collected, Artificial Intelligence is able to determine information or characteristics which can inform us of repetitive behaviour or predict statistically probable actions. Consequently, organisation and planning can be undertaken with ease to improve the efficiency of the supply chain. For example, ordering a calculated amount of stock in preparation for a busy season can be made using much more accurate predictions - contributing to less over-stocking and potentially more profit. As a result, analysing behavioural patterns facilitates better management and administration, with a knock-on effect for improving processes. Streamlining operations Using image recognition technology, Artificial Intelligence enables quicker processes that are ideally suited for warehouses and stock control applications. Additionally, transcribing voice to text applications mean stock can be identified and processed quickly to reach its destination, reducing the human resource time required and minimising human error. Artificial intelligence has also changed the way we use our inventory systems. Using natural language interaction, enterprises have the capability to generate reports on sales, meaning businesses can quickly identify stock concerns and replenish accordingly. Intelligence can even communicate these reports, so Data reliably reaches the next person in the supply chain, expanding capabilities for efficient operations to a level that humans physically cannot attain. It’s no surprise that when it comes to warehousing and packaging operations Artificial Intelligence can revolutionise the efficiency of current systems. With image recognition now capable of detecting which brands and logos are visible on cardboard boxes of all sizes, monitoring shelf space is now possible on a real-time basis. In turn, Artificial Intelligence is able to offer short term insights that would have previously been restricted to broad annual time frames for consumers and management alike. Forecasting Many companies manually undertake forecasting predictions using excel spreadsheets that are then subject to communication and data from other departments. Using this method, there’s ample room for human error as forecasting cannot be uniform across all regions in national or global companies. This can create impactful mistakes which have the potential to make predictions increasingly inaccurate. Using intelligent stock management systems, Machine Learning algorithms can predict when stock replenishment will be required in warehouse environments. When combined with trend prediction technology, warehouses will effectively be capable enough to almost run themselves negating the risk of human error and wasted time. Automating the forecasting process decreases cycle time, while providing early warning signals for unexpected issues, leaving businesses better prepared for most eventualities that may not have been spotted by the human eye. Big Data is continuing to transform the world of logistics, and utilising it in the best way possible is essential to meeting customer demands and exercising agile supply chain management. If you’re interested in utilising Artificial Intelligence and Machine Learning to help improve processes, Harnham may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. Author Bio: Alex Jones is a content creator for Kendon Packaging. Now one of Britain's leading packaging companies, Kendon Packaging has been supporting businesses nationwide since the 1930s.
29. August 2019
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.
22. August 2019