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 News & Blogs portal or check out our recent posts below.
Writing a new resume can often to be a more challenging task than you initially think. Accurately portraying your skills, breadth of experience and knowledge within a few short pages is a tough task. There are a number of online guides about how to write a good resume, along with a variety of opinions on what works; questions around the latest style, layout and how many pages it should be, make this a very subjective topic.
We have written the guide below to give you some useful tips around writing your resume, based on feedback from employers about what they expect to see on a Marketing Analytics & Insight professional’s resume. Consider this quote from one of our clients:
“…the point of having an analyst in a business is to accurately condense and analyze large volumes of data and draw out the relevant pieces of information that can have an impact on a business. An analyst should be able to sift through irrelevant information and draw everything together to highlight relevant information in a compelling way. If an analyst isn’t able to have the same approach with their resume and draw out the information that is relevant and discard the rest, it doesn’t give a good impression or an indicator that they will be an effective analyst.”
So how do you go about making sure that your resume does give the right impression and get you that interview opportunity?
A good structure should typically follow this order:
However, don’t be afraid to deviate from this structure in order to demonstrate your relevance for a particular position more effectively. If you are a recent graduate, with a relevant mathematical degree, but little or no relevant employment history, you are likely to have more success by highlighting your relevant academic background above your employment history.
Use a clear layout and include headings to separate each of
the above sections. Within each section use bullet points to define your role,
responsibilities and skills rather than long paragraphs full of commas. This
will help to make the content far easier to scan for key information and is
more likely to grab the attention of the employer.
Keep the whole document relatively short, 2-3 pages maximum.
Pay attention to detail and spelling: many of our clients reject applications based on this – remember our client quote! Ensure all information is accurate; dates, company names, skills, technologies used and don’t be shy of Spell Check.
Make sure all formatting is consistent: we recommend you use the same font throughout the document and utilize bold to highlight subsections and headings. Typically, fonts such as Arial or Times New Roman are acceptable.
The content should be clear and concise, but with enough information to give the employer a solid understanding of what your role entails and what your responsibilities are.
It is useful to give a brief introduction to the company and / or team to add context, but essentially the employer is going to be more interested in hearing about your skills and responsibilities and not those of the team in general.
With regards to the data sets, statistical tools and techniques you typically employ, specific information here is also key. For example;
You highlight that you use SAS in your current role. Try to elaborate on this i.e. Are you using Enterprise Guide, Macro, Base? Do you write your own code or employ more drag and drop techniques?
You also work with propensity models. Did you build the model or are you working on existing models and validation? Do also have experience of clustering, segmentation, regression or similar techniques?
You work with a range of data sets. What kind of data is it; Transactional, campaign? Make sure you explain. It’s also important to remember that large data sets are typically appealing to companies; therefore ensure you refer to the size of the data sets you’ve been exposed to. For example, how many rows of data do you typically deal with, or how many campaigns are you used to running each month?
Adding these snippets of key information won’t take up a lot of valuable space, but will help give your prospective employer a more detailed understanding of your skills and level of competence, ultimately, helping boost your chance of securing an interview.
Lastly, tailor your resume for each role you are applying for:
Carefully read the job adverts and descriptions and highlight relevant pieces of information to showcase your skills and experience that most suited to what the company are looking for.
Likewise, amend your personal statement for the same reason. Don’t be the person who applies for a Customer Insight Analyst position with a Retail & FMCG consultancy when your personal statement still says you are looking for a Marketing based role in a Client side Financial Services organization.
Remember, resume's are meant to be factual but they are also a tool to sell yourself, so make the content interesting, relevant and engaging – this could be the only opportunity you have to convince an organization that you are someone they want to interview and help you stand out from the other applications they receive.
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 News & Blogs portal or check out our recent posts below.
Over the last four decades, we’ve feared change. Technophobia. Cyberspace. Smart devices. AI, Robotics and Automation. Each of these transformative shifts have changed our lives in one way or another. But there is a new, unexpected and desperately needed change already in play; putting the human back in our lives. Human Resources. Human-centric customer service. Humans in cooperation and collaboration with smart technology. Both in B2B and B2C businesses, putting the human back in focus is imperative to success. Consider Netflix. How it began, how it’s evolved, and how its efforts are seemingly leading the way for next gen personalization. Think: If you like this, then you may like (insert service or product here). Amazon does much the same. Putting the Human Element Back in CX When you call customer service with a concern or problem. What happens? Either there’s no phone number at all and you’re forced to send an email which you hope gets read by a person. Or if you do call, you push buttons trying to figure out which branch of the tree will get you to the correct person. Chatbots have been one answer but they really only alleviate acknowledgement. We’ve all called a customer service number and spoken to two or more people about our issue. Bill Paterson, EVP of Salesforce, suggests a four-point, human-centric customer service engagement strategy, to help solve the problem. In addition, his article takes a deeper dive into putting the human back in customer service. At the heart of the matter is putting Emotional Intelligence, care, and empathy back into the equation. Technology may be how people reach out, but it’s a human they want to speak to and connect with. When the two are paired, there’s a much better chance of success. And repeat customers. Pairing Machine Learning with a Human-Centric Touch While strategies and metrics still have a big role to play, there are other ways to measure customer success. Data gathered from your customers will only get you so far, but the human element, the human connection, supported by technology, is the next shift in Digital Transformation. Machine Learning models can help predict what customers will want or need, but meaningful customer relationships are just as vital. It’s this pairing which can generate great service and scalability of today’s modern business. Though there is a strong underpinning of engineering components in building models, only a portion involves code. Much of the effort goes into the pipeline and workflow systems and infrastructure. It’s at this systems level, Data Scientists can focus on design and implementation of production. This strategy ensures that before building good models, a good foundation must be laid. One portion of this workflow has been called the ‘art of Machine Learning’. The ‘Art’ of Machine Learning Data Scientists and Machine Learning Engineers have any number of ways to solve a problem. Dealing with such vast amounts of Data within a model is not unlike determining how to scale for a website which needs to handle large fluctuations in web traffic. The nuances of technology within the realm of human experience is an artform. Though in the future, most engineering challenges will be automated and open-source will be a go-to framework. As tools improve and ETL processes improve, ML Engineers and Data Scientists will get the opportunity to focus more on models and less on systems. But beyond the artform of experimentation and intuition is the growing trend for soft skills in tandem with technical skills. Those who can lead a technical team, who can communicate to non-technical professionals, and still have the Emotional Intelligence to navigate the human psyche. It’s these individuals who will be ready for the next step in leading businesses into the next generation of customer service. Ready to take the next step in your career? Take a look at our current vacancies or contact one of our expert consultants to learn more. For our West Coast team, contact us at (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to firstname.lastname@example.org.
20. February 2020
If you’re a small to mid-size business and think cyber criminals only go after big business; think again. It’s just as important, if not more important for you to have privacy plans in place. This goes way beyond GDPR and state-to-state rules, this is about how you care for your customers personal information. The return on investment will set the tone for future years of your business. After all, according to a 2018 report by Verizon, 58% of cyber-attacks targeted small business. While it may seem counter-intuitive and larger businesses are bigger fish to go after, they can be difficult to get into. After all, they’ve got the resources to protect their customer’s Data and are hyper aware of what it can be to their business if they don’t. Smaller and mid-size businesses generally don’t have the resources of the larger businesses, and may not focus on cybersecurity like they should which leaves their business wide open for cybercriminals. Chinks in the Armor of Your Data Cybercriminals excel at finding “chinks in the armor” of your Data. They’ll use any advantage to break in from the usual hacking and malware to physical breaches. One improperly secured device can be just the entry they need into your entire system. What can you do? Be focused in your approach to Data security. Many small businesses tend to put out fires, rather than have a focused strategy. And each approach to tighten security can lead to more opportunity for hacking.Communicate your strategy to every member of your team. Something as small as clicking on the wrong link can lead to a Data breach.Train your staff on measures they can take such as to not click on a link they’re not expecting, to check email addresses and ensure they’re approved or white-listed as okay to access. The more aware your staff are, the better able they’ll be able to help ensure the security of your business’ Data. While staff may be on the front lines, this also requires a commitment from senior executives as well. Understand that just because you’re not dealing in billions of dollars, you may actually be at greater risk. Why? Because unlike the larger companies, your business may not survive the fallout of a cyber-attack. How to Protect Your SMB You can protect your business by creating a Data Security Strategy and consider the following: Encrypt your data;Authenticate your users by either a 2-step verification process or having them enter some kind of code;Authorize access to trusted sources. Encrypting Data helps protect the private and sensitive information and makes it unreadable without the correct key. To ensure only those who are trusted sources have access is through authentication. Authentication can include username/password, code, tokens, phone number, and image association such as click only the boxes with pictures of street lights or stop signs. This helps your business control who has access and gives you tighter rein over who sees sensitive information and what they can do with it. By defining the rules and regulations of access to information, training your employees to be aware and what to do to ensure security, you can strike a balance of increased security and transparency to your customers. In other words, the efforts you go through to protect their Data will put you ahead of the competition as you make inroads toward a Data privacy strategy while others take action as things happen. One Final Thought Ensuring your business’ Data is protected and detecting times when it may have been breached is increasingly important to help minimize damage. One issue SMBs face is that it may take longer to detect if there isn’t a Data security plan in place. The more quickly you can detect an issue, the more quickly you can reduce its impact and the more quickly and effectively you can respond, the better. Interestingly, smaller businesses tend to have a better overall picture of their assets than larger businesses. This can be a boon when you communicate your new cybersecurity strategy to your teams and offers a significant return on investment of your resources. If you’re interested in Big Data and Analytics, we may have a role for you. Take a look at our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
14. November 2019
Earlier this month I had the pleasure of speaking at ODSC East, as the best future talent Data Science talent gathered together to discuss the direction of our industry. With Data Science becoming such a broad term that covers a number of functions, and with the rise of new areas such as Blockchain, AI and ML, I wanted to talk about what it actually means to be a Data Scientist now, and in the future. With this in mind, we conducted a survey over the course of the event where we asked what Data Science meant to the people there. Here’s what we found out: WHAT IS DATA SCIENTIST, ACTUALLY? Every company thinks they need one, and every analyst wants to be one, but more and more job titles that are not necessarily Data Science are now being billed as Data Scientists. In fact, when we asked people what they considered their job title to be, regardless of experience, Data Science came out on top: Data Scientist: 58% Data Analyst: 22% Machine Learning Engineer: 10% Business Intelligence Analyst: 9% However, from my experience, this is not necessarily accurate. I once worked with the Senior Manager of Data Science in a very well established Retailer. He’d been there for less than one year and was already on the job market. In his interview he had been told that the company were fully behind investing in a top-class Data Science department but had actually ended up managing a team of people who were building dashboards creating reports for all areas of the business. This is much less Data Science, and much more Business Intelligence. This confusion is quite typical within the industry and frequently needs to both unhappy employers and employees. MORE THAN JUST TOOLS One common mistake when it comes to misidentifying Data Scientists is a result of people focusing on the tools people use. Whilst both Data Scientists and Marketing & Insight specialists might be skilled up in Python, R and SQL, their methodologies are significantly different. When asked to define a true Data Scientist at the event, 73% of people agreed the definition is: “A person who uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.” Companies who panic about needing a Data Scientist to keep up with their competitors often ignore these crucial points and end up listing every tool on a job spec. Frequently those who claim they want a Data Scientist actually want an Insight Analyst who can understand how customers behave, what they respond well to, what they’re talking about on social media, and how this unstructured data can be used to help their business make better decisions. WHAT DOES THIS MEAN FOR ME? For someone wanting to work in the Data & Analytics field there is one key rule: Know Yourself. Think carefully about aspects within your Science, Operational Research, Statistics, and Analytics in general that you enjoy and how you can work them into your career. If you’re in college and just starting your career, don’t limit yourself by the sectors you think you have to work in; enjoy gaming? The gaming industry uses Data to make characters more lifelike, make sure they move in real-time and ensure that they play in a realistic way. Just as crucial, however, is having an understanding of what the analytical teams around you do. Consider what roles they play in your business and how you are all interlinked, whilst being aware of the unique differences between roles. And, outside of analytics, those who understand what impact their work has on a business will always stand out amongst a crowd. Essentially, don’t let yourself be limited by the title of Data Scientist. There are hundreds of roles within Data & Analytics so think about which one is right for you, rather than following the crowd. If you’re looking for your next opportunity in Data & Analytics, or are looking to build out a team, take a look at our latest roles or get in touch with one of our expert consultants: For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org. Or, if you'd like to talk to me directly about anything I've talked about above, feel free to drop me a line at email@example.com.
30. May 2019
Just because pricing deals with numbers, it doesn’t mean it’s exclusive to the financial sector. In our last few posts, we focused heavily on the role of Pricing Analyst, what it is and how to get there. This type of analyst role is more often found in the marketing arm of many companies and might also be known as Behavior Analyst, Customer Analyst, or something similar. However, there is another type of analyst sometimes confused with Pricing Analyst which falls squarely within the Finance sector. These roles might boast titles such as Risk Analyst, Financial Analyst, or Actuary. Often, it isn’t the title that speaks to the particular strengths of one type of role over another; it is the responsibilities and skill sets documented within the job description. Like Pricing Analysts, these professionals deal with numbers and pricing. However, their focus is on models, such as those required for mergers and acquisitions or how to set health insurance premiums looking at risk. Looking for a Low to No-Risk Gig? Actuaries are in high demand. As a profession, it is one of the most diverse and tends to be more open to women and under-represented minorities. Though the focus is often on insurance and pension programs, Actuaries can find work in a number of industries including consulting firms, hospitals, banks, investment firms, and government. As advisors who manage risk portfolios while analyzing historic and current data, these professionals are business-minded people with a mathematical basis. Using mathematics, statistics, and financial theory, they analyze the financial consequences of risk. The Masonic-esque Levels of Becoming an Actuary For individuals who are numbers focused and are interested in using their data, technical, and mathematical skills coupled with business acumen; the role of Actuary might be the perfect fit. However, there are steps or levels which need to follow to enter the profession. These are exam-based and work-experience levels and your salary increase incrementally with each step. To begin, a graduate with a high GPA and one exam under their belt may find the role quite lucrative. Each exam leads to the next level and enters you into an Actuarial Society. Depending on where and what you want to practice will determine which society you’ll sit the exam: Society of Actuaries (SOA) – focus is life and health insurance, pensions, and employee benefits. Casualty Actuarial Society (CAS) – focus is automobile, fire, and liability insurance as well as worker’s compensation. American Society of Pension Actuaries (ASPA) – focus is those in the pension field, particularly in relation to federal and state governments. Each organization has its own exams and competition is fierce. Qualities sought beyond a high GPA and actuarial exam include: Good communication skills High technical ability A wide background from mathematics and statistics to the liberal arts Actuaries and analysts with an eye toward the financial and insurance sectors use their statistical skills to research, network, and connect the dots between discerned variables. The research begins with statistical modeling. Connect the Dots with Statistical Modeling In statistical forecasting models, the information gathered helps analysts make statements about real outcomes which haven’t yet come to pass. The model can then help identify what might influence these variables. An Actuary, Financial Analyst, or Risk Analyst may use a: Merger Model (M&A) – This model is most often used in investment banking and corporate development. Think mergers and acquisitions. After all, someone has to decide the value of each company, then the basis of that value once they’re merged. Complexity varies widely in this model. Budget Model – This model is used in financial planning and analysis and helps set the budget for the coming year and the years to come. Focused heavily on a company’s income, these budgets are designed on a monthly or quarterly basis. Forecasting Model – This model is used to build a forecast of the budget model. Think of it as a building block as companies structure their budget and strategies using one or a combination of these models listed. Sometimes, the forecasting and budget model are combined. Sometimes they’re kept separate. These are only three of the ten types of models used in financial planning and analysis for any number of firms and industries. But, it’s the people behind the numbers who help businesses navigate what is best for their client, customer, and bottom line. An Actuary is just one title those interested in the mathematical and statistical applications for business might find interesting. And like many of those in the Data Science field and higher tech applications, this role is in high demand. Are you the one companies are looking for? If you’re interested in finance, modeling, statistics, Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614-4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796-6070 or send an email to email@example.com.
07. February 2019