Marketing Science Consultant
London / £45000 - £50000
£45000 - £50000
MARKETING SCIENCE CONSULTANT
A well-established Agency looking to hire a Marketing Science Consultant who will get the chance to work with some of the biggest brands across the Media, FMCG, Wellness and Retail space.
The position is situated within the agency's MI team that provides consultancy services to many of their clients. They provide a range of solutions tailored to their clients needs, including econometric modelling, channel optimization, audience segmentation and more!
- Help lead and develop client relationships
- Develop strong relationships across the agency's varies teams, acting as the expert for your field
- Excellent communication and presentation skills - able to tell stories with data, and effectively articulate the commercial value of analytics to clients and agency teams
- Be able to translate all data and analytical outcomes to a real-world application generating commercial benefit to clients
- Contribute to and represent the marketing intelligence team in new business pitches, producing documents and presenting as required
- Contemporary approach to analytics with an emphasis on market mix modelling, digital attribution, location and segmentation analysis using a wide range of data mining, statistical and machine learning techniques for creative problem solving
- Ability to create (or support in the creation of) robust, scalable solutions for a new definition of media measurement
- Able and willing to be hands on when necessary, with the delivery of modelling and analytical projects
- Ensure all output is technically excellent and addresses all of the client needs - including the ones they may not have articulated fully. Ensure that all output is fit for purpose, consistent and accurate
- Responsibility for client and project management, setting project milestones, including delivery of projects to deadlines
- Continually strive for excellence in everything we do and encourage our team to be the best they can, all the time
- A collaborative approach - working with other Specialist Services, agency teams and the wider team to help feed into the development of new approaches and techniques, identifying additional potential Annalect revenue streams in the process
You don't have to tick all the following boxes, but we'd love to hear from you if you:
- 5+ years knowledge and experience with: Marketing Mixed Models, digital attribution, classical and Bayesian statistics and simulation techniques.
- Experience with one or more Data Science languages: R essential, Python desirable.
- Strong data visualisation skills - ideally R Shiny and Markdown - to represent complex data relationships and deliver compelling analytical output
- Experience with media data, with experience with platforms and customer data from either the media agency, technology or brand side.
- Understanding of ad technologies and how they are evolving in the new media landscape and the roles of different addressable tactics - TV, CRM, mobile, search, etc.
- Manage, mentor, and motivate direct reports to encourage their professional development while providing hands on, daily supervision and guidance.
- Highly organised, self-motivated, and proactive, with the ability to manage the delivery of multiple analytics projects across clients effectively from proposal to client delivery
- An understanding of digital media, ad tech and programmatic an advantage
- Significant experience of working in data analysis in a commercial environment, ideally within a marketing, media and/or communications context
You will receive:
- Up to £60,000
- And more!
How to apply
Please submit your CV to Emma Johnson at Harnham
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.
Why Should You Care About Data-Driven Marketing? | Harnham Recruitment post
Marketing has been undergoing a fundamental change for some time. Elite marketers have been rethinking and reiterating their strategies, using increasingly sophisticated data. and this trend has been further accelerated by the pandemic.Consumer behaviour has changed significantly since the pandemic began. Between March and August 2020, 70 per cent of consumers tried new digital shopping channels. Such significant changes have rendered many existing data models invalid. Data-driven marketing offers new insights into consumer behaviour and can render huge impacts in refining and enhancing marketing strategies. So, why should you care about data-driven marketing? Offers better clarity about the target audience 67 per cent of lead marketers agree data-based decisions beat gut instinct. Data-driven marketing allows marketers to quickly filter through data and determine the most relevant and accurate action to take. With the right data, marketers can assess customer data to predict behaviours, identify buying patterns and spot emerging trends. Data-driven marketing can also reveal new channels and open up new avenues which organisations can use to engage with audiences and increase revenue. Increases revenue The last 18 months have been tough for businesses, yet through the use of data insight, marketing teams have been able to get ahead of emerging trends. Data-driven campaigns have pushed significant customer acquisition. Better insight into consumers and the channels they use enables organisations to improve their marketing strategy. Indeed, companies that deploy data-driven marketing are six times more likely to remain profitable year-over-year, and 78 per cent of organisations agree that data-driven marketing increases customer acquisition.PersonalisationIn the modern world, advertising is everywhere, and it is endless; consumers see it on their phone, their TV and even on their way to work. Without target advertising campaigns, organisations risk aggravating consumers. 74 per cent of customers already feel frustrated by seeing irrelevant content from brands. To stand out, marketing channels have become more complex. Marketers need to remain creative to capture consumers attention and data driven marketing can help achieve this.Data-driven marketing allows businesses to target specific demographics and user groups at an individual level. By targeting specific user groups at an individual level, marketers are able to use personalised marketing campaigns to build stronger and more meaningful connections with potential customers.With individual customer information, brands can segment a target market and ensure personalised messages are falling into the right place. Data-driven marketing is also able to identify potential customer triggers and create a holistic view of the target audience. This style of personalised marketing campaign makes for a more positive customer experience, and therefore represents excellent return on investment.Data has the potential to become an incredibly valuable resource in marketing. Data soothes the pain points which many marketers face on a day-to-day basis, and help teams to refine, enhance and improve strategy. In a post-pandemic world, data-driven marketing will undoubtedly be essential. To stay competitive, internal marketing and insight teams need to start taking notice of data-driven marketing. Here at Harnham, we understand the importance of data-driven marketing to determined campaigns and guide decisions. So, if you’re looking for your next opportunity or to build your Marketing & Insights team, we can help. Take a look at our latest marketing and insights jobs or get in touch with one of our expert consultants to find out more.
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.
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