Business Director (Marketing Science)
London / £70000 - £100000
£70000 - £100000
UP TO £70,000-£100,000
Harnham is working closely with a well-established Media Agency on a Business Director (Econometrics) role. This particular role sits within a specialist division that works with private sector clients and the government.
You will be a team player and be able to successfully manage a small team to maximise the efficiency and effectiveness of a range of projects for the government and private sector clients.
- Have good experience of working in marketing effectiveness, strong analytic capabilities and effective project management skills
- Be involved with day to day management of multiple client projects, managing a team and working to improve processes and templates for increased efficiency
- Be coaching and development of junior analysts working on reporting, visualisation, and analytical projects along with line management responsibility - the professional and technical development of more junior team members
- Lead and develop internal stakeholder relationships within, acting as the expert for your field, helping feed into the development of new approaches and techniques, identifying potential analytics opportunities
- Have excellent data communication and presentation skills - able to tell the stories with data and translate data and analytics outcomes to a real-world application generating commercial benefit to clients
- Lead and or support on areas of innovation within Benchmarketing team - be open minded and curious, with an ability to derive innovative solutions for clients by applying creative thinking to analytics problems
- Contemporary approach to analytics with an emphasis on simple analysis of high level strategic datasets, often category cross sectional analysis, some marketing mix modelling, segmentation analysis using a wide range of datamining, statistics and machine learning techniques for creative problem solving
- Commercially aware of all accountability and client requirements from a target, value and commercial perspective
- Able and willing to be hands on with delivery of modelling and analytics projects
- Ensure all output is technically excellent, visually clean and accessible, and addresses all of client needs
- Responsible for project management, setting project milestones, including delivery of projects to deadlines
You don't have to tick all of the following boxes but we'd love to hear from you if have any of the following:
- Highly organised, self-motivated and proactive, with the ability to manage the delivery of projects across client effectively
- Strong attention to detail is essential with clear/concise communication both verbal and written to a number of audiences
- Experience within MMM/Marketing Mix Modelling
- Proactive, collaborative and positive attitude with all stakeholders
- Good understanding of classical statistics, machine learning and simulation techniques
- Experience and passion for improving r/python/sql/excel vba
- Strong data visualisation skills welcomed - some/good experience of using
- r-shiny, google data studio, tableau, Power BI to represent complex data relationships and deliver compelling analytics output
You will receive:
- Up to £100,000
- Great holiday allowance + Bank Holidays + buy/sell option
- Life assurance
How to apply
Please apply by submitting your CV to Emma Johnson at Harnham
Why Marketing Teams Need to Fill Their Data Skills Gaps
Data can be leveraged in a myriad of ways and be beneficial to numerous business functions.
In marketing, for example, data is playing an increasingly important role in helping brands get closer to their target customers, which ultimately improves the bottom line. Businesses that use data-driven marketing strategies have five times more ROI than those that don’t.
Despite this potential, a new survey has revealed that data analytics is one of the biggest skills gaps in marketing departments. Below, we break down this new research and explain why it’s crucial to fill your company’s data skills gap and build a data-driven marketing team.
So why does this skills gap matter?
The recent research revealed more than a third (34.4%) of the 3,000-plus respondents identified a lack of data analytic skills in their marketing department. For B2B marketers, the figure drops to 29.9 per cent, while it’s 34.6 per cent for B2C marketers, and jumps to 39.6 per cent for businesses with a mix of both.
These findings are particularly pertinent as marketing isn’t a department that operates within a bubble, rather it has its tendrils in every part of an organisation, so when marketing isn’t functioning as optimally as possible, neither is the business.
Businesses that are not harnessing the insights that data analysis offers, are missing out on the ability to understand and meet their customer’s preferences. Making decisions that are not grounded in data means that a business is operating in the dark – throwing ideas at the wall to see what sticks rather than already knowing what will work because the data has told them so.
Many companies have realised that it’s no longer good enough to guess what customers might want or need from a product or service, but to instead have hard evidence to back up these choices. A data-led marketing strategy can revolutionise marketing efforts in numerous ways such as:
Behaviour analysis and personalisation
By analysing a customer’s behaviour, such as their e-commerce and website browsing habits, marketers can ensure that the businesses’ landing pages, calls to action and other marketing tools are working as they should be, and use this data to better tailor content and improve the customer experience.
Behaviour analysis might include examining customer interactions, such as where and when they click on a website, even down to which pages consumers are lingering on for longer. The content you are producing might be incredibly insightful and smart, but that’s irrelevant if customers aren’t reading it. Once you have understood where people do and don’t spend time and which content attracts the most engagement, assets can be shaped to scoop up people who might otherwise leave a site, further entice already interested parties and inform other marketing activities.
For example, if you’re a business that sells clothes, you can use data analytics to determine which colours and styles are most popular among your customers and create content such as fashion tips or trend reports including these colours and styles.
Through monitoring the current behaviour of customers, businesses can also more easily identify when and how their preferences change. For example, if visitors to written pieces are dropping off, you could consider incorporating more video content. Reacting to the subtle changes in customer behaviour can help companies to maintain their position in the market and increase their revenue by tapping into new pools of customers.
Predicting customer patterns
But data isn’t just for making better in-the-moment decisions. It can also help to pre-empt future customer behaviour, allowing businesses to make proactive decisions based on previous trends, rather than acting reactively.
Predictive analytics is the use of data algorithms and techniques to define the likelihood of future events or results, based on historical data from customer habits. It allows marketers to forecast a customer’s “next move”, such as which consumers are most likely to buy again, and therefore prioritise customers.
Based on previous patterns of behaviour, businesses can predict website engagement points where, for example, a customer may convert, but also areas where consumers might lose interest or drop off – friction points such as filling in a form. This information enables businesses to make choices that ensure that the customer experience is as smooth and effective as possible.
How can this skills gap be filled?
The effectiveness of data analysis is dependent on talent being able to carry it out. At Harnham, we specialise in data hires for marketing. In other words, through experience, our consultants have built a comprehensive picture of what marketing teams need when it comes to data marketing talent. When it comes to hiring a data marketing professional there are a wealth of skills to look for, including:
- Being a problem solver – a candidate who can identify logical ways to overcome problems and offer solutions.
- Having a good grounding in coding languages such as SQL. Whilst it can be beneficial to have more advanced modelling skills using R or Python, some companies will have data science teams to support on this side.
- Experience with visualisation tools and with programs such as Tableau or Looker – which can be hugely valuable in hitting the ground running.
Most crucial, however, is the ability to tell a story with the data and make something complex easy to digest. During an interview, businesses can identify how someone translates recommendations and if they are able to recognise and illustrate the commercial impact that their work will have.
If you’re interested in applying your data skills to a role in marketing or are looking to bolster the success of your business by hiring a Data & Analytics specialist, you’ve come to the right place contact our team today.
Data Science Interview Questions: What The Experts Say | Harnham Recruitment post
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 QuestionsOnce 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 LearningAs 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 AnalysisMachine 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 MathematicsAs 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)
ProgrammingWhen 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 QuestionsCapabilities 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.
The German Market: Businesses Need To Adapt Or Miss Out On The Best Tech Talent | Harnham Recruitment post
After moderate market movement in the spring, the tech recruitment market in Germany is seeing more significant movement now, as businesses align their budgets and headcount for 2022. But there remains a real shortage of tech talent in all parts of the sector, from Data-Science and Software Engineering to Data Intelligence and Marketing Insights.Recent research conducted by the Berlin office highlights that hybrid and remote working options, as well as growth and upskilling potential, are the most important deciding factors in the German job market right now. The only distinct difference between those surveyed was in long term financial incentives – men preferred a workplace bonus, women regard a workplace pension and insurance benefit as a bigger priority when considering a job move. That aside, flexible working and maintaining a good work-life balance are set to stay. In this respect, Germany faces a particular challenge as culturally, onsite teams and face-to-face working relationships have always been of high importance to efficient operations. In addition, many players need to rely on a hybrid working model asking employees to come in at least some of the time which is additionally challenging due to the remote location of a lot of companies. Added to this, the country specific issues that Germany faces are likely to present ongoing challenges as we move into 2022. Germany has the broadest range of company type, size and structure in the world and the wide cultural and ethnic diversity creates a non-homogeneous market with micro-markets that need a bespoke approach when it comes to tech recruitment.Big Businesses slow to react The speed at which German businesses can react to environmental change is affected by high employee participation in Trade Unions and works councils (Betriebsräte). Change can be slow, even under normal circumstances, regardless of how much or fast leadership want to act. Listed businesses find it difficult to turn the ship around quickly. The logistical challenges combined with the need for larger organisations to shift their cultural mindset and tech environments are significant barriers to change.At the other extreme, however, SMEs that are much more agile and flexible are seeing this time as a real opportunity to attract the best tech talent, many of whom were more interested in the stability of roles in larger organisations. But times have changed, people want more control over their working conditions and greater transparency regarding outlook and overall company strategy when it comes to the data journey. More than ninety per cent of German businesses are SMEs (the highest ratio in the world) which makes the recruitment market exciting right now. It continues to be a candidate led market. The pandemic effect on BusinessEmployers were affected differently during the pandemic. Tech service providers, e-commerce businesses and retailers that already had online sales operations saw business go through the roof as consumer behaviours changed and shopping migrated online. Digital Marketing and Data Insights roles were in demand as retail businesses scaled up in response. This huge growth combined with the shortage of candidates as those in secure jobs sat tight. Those that did move, became quicker in their decision-making. Where we were used to seeing a steadily moving market, candidates taking their time deciding whether a role might right for them, things sped up. Work-life balance, location and job security were all major factors in the market, so those smaller, more agile clients that were quick to offer these things became very attractive to candidates who might have otherwise taken their time.Businesses that are less invested in their tech infrastructure or failed to upscale the backend were hit particularly hard. Some innovative start and scale-ups providing solutions at the point of sale such as hard- and software, went into hibernation. Where previously data architects and data engineers were not regarded as critical to business growth due to a focus on adding features and growing the userbase, are now quickly becoming integral to operations. Now the exponential growth phase has plateaued, the last 6 months has seen businesses investing in data initiatives to transform their operations. Those strategic businesses with the foresight to address this were able to weather the storm, those that did not faced real pressure, some even went into liquidation. The tech start-up space has been largely dormant as venture capital and private equity was hard to come by. We expect to see that pivot both in response to the pandemic spawning entrepreneurs and as gaps in the market for digital solutions are realised. Future-ProofingHaving taken stock, and with lessons learned, those businesses that have survived the pandemic are future-proofing, investing in data initiatives around more robust infrastructures. Data Engineers, Software Engineers, DevOps and platform teams are high in demand and the recruitment market is running hot. The more classic customer-focused roles are also being advertised – Data Scientists, Social Media Analysts, Multi-Channel Marketing, Data Insights.New Roles in TechAs mentioned by my Nordic colleague Amanda Snellman there is an interesting evolution in tech. Brand led businesses are looking to their marketing teams to find ways to maintain a competitive advantage in the market are actively seeking talent to bridge the gap between Data and Marketing where candidates can speak the language of both disciplines. This is one of the more positive outcomes of the pandemic – silos are being broken down and operations are moving towards multi-disciplined product teams that are charged with budgets and responsibilities. These hybrid roles (Data Managers, Product Managers, Product Owners and similar) are falling out of the need for candidates who can understand the analysis, see the potential data can have in responding to consumer needs and who are able to transform those insights into actionable measures that can move businesses forward in a meaningful way. Data Scientists and Analysts who have a real understanding of what data can do to solve consumer problems and help a business grow. The Ripple EffectThe ripple effect of the pandemic will be felt for years to come. Currently, businesses are reacting out of necessity. The pandemic has resulted in many data initiates being prioritised. Those tech projects which may have taken several years to reach the top of the business agenda are now a huge focus. Communication is easier, and online meetings facilitate decision-making. But with home and work lines becoming more blurred and employees being looped in 24/7 the next pandemic may be burnout. Is remote working here to stay?Absolutely yes, despite the downsides. There is a slow realisation that if there is an internet connection, and a candidate can work, they can be based anywhere. Big businesses need to get on board with that to secure the best talent. There has always been remote working in tech and German businesses have long looked to other countries to fulfil their tech recruitment needs. Change was already happening; the pandemic has just exaggerated the curve. How can businesses make themselves more attractive in 2022?Going into 2022, choice will be key. Candidates have been in short supply for some time and as the German market approaches year-end this remains unchanged. As always, we continue to be selective in who we send to interview, which our clients appreciate, and most we put forward get to interview. Once at this stage, if hiring managers be open-minded to candidates’ requirements and respond accordingly then there will be measurable success in recruitment. The candidate led market is here to stay for some months yet.Looking to build out your data team? Get in touch with one of our expert consultants. Looking for your next opportunity? Check out our Data jobs in Germany.
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