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There are a wide variety of Interview techniques that are typically employed by organizations during recruitment processes in order to identify the best person for their role. One of the most popular techniques you are likely to experience during your job search is a CBI (Competency Based Interview).

Competency Based Interviews are often a successful technique for both the company and you as they provide an opportunity for you to be assessed in an objective manner, based purely on what is necessary to be successful in the role. The interviewer will evaluate your answers against the competencies required for their role, while you can assess whether the job matches your key skills and attributes.

Expect to be asked a range of questions that concentrate on the most important parts of your past experience, focusing on the behaviors that you demonstrated within certain situations. Some examples of typical areas often assessed are: Personal, Motivation, Decision-making, Stakeholder Management, Organization and Management.

 

What types of question should you expect?

Examples of the type of question you may be asked are:

‘Tell me about a time when ......’

‘Describe an occasion when .....’

‘When has it been important to .......’

This style of question may be new to you and can seem quite formal as the interviewer will also be taking notes as well.

What CBI does mean is that you will be expected to give evidence based answers.

 

The importance of learning the STAR response technique

Another style of interview technique, very similar to standard CBIs, is STAR (Situation, Task, Action, Result) which again ensures the interviewer obtains all the relevant information about a specific capability that the job requires.  It is also a good structure to utilize when answering CBI questions. This format is said to give a good insight into future on-the-job performance of a particular candidate, for example:

SITUATION:

You will be asked to present a recent work challenge/objective

TASK:

What did you have to achieve?

Action:

What did you do to achieve the challenge?

Result:

What was the outcome of your actions and did you meet the objectives of the challenge?

Tips to help you navigate successfully through a CBI

Here are a few specific tips on making the most of Competency Based Interviews:


Know your resume:

As highlighted above, the CBI will require you to draw from personal experience, often work experience to demonstrate key attributes. You are likely to refer to particular roles you’ve hold, projects you’ve worked on and situations you’ve faced with previous employers, so it is important to be able to give thorough and accurate answers that accurately reflect what is on your résumé.

 

Prepare and prepare again:

Ensuring you have thoroughly researched the company, values, recent projects, the role and interviewer are all aspects you should be well versed in by the time you attend any interview. However, for a CBI you will also need to provide evidence based examples from your own experience. There can be nothing worse than having to think of a good scenario off the top of your head, so mentally prepare some solid examples for competencies that are likely to be relevant for this role. We have already highlighted some popular areas that are often assessed, however think about the role and company in question.

For example, the position you are applying for is a management role, requiring you to influence key stakeholders and deliver actionable data driven recommendations to increase ROI. Based on this, it is therefore likely that you will be asked questions around management, influencing others, commercial awareness and/or stakeholder management.

 

Think commercially:

This is an area people often fall down on in interviews in general. You may be attending an interview for a technical role, however more often than not employers are interested in well-rounded people, who demonstrate good communication skills and commercial awareness, in addition to a high level of technical competence. For example, if you are describing a time that you built a predictive model using SAS, also think about the ‘so what?’ linked to that. Once you have explained the actual process, also think about what impact it had on the business. What were the objectives and the more importantly the results?

 

It’s all about you:

Remember that the interviewer is interested in finding out about you and what you have achieved, not about your team, project or manager’s achievements. So don’t be too modest and remember to talk about the part you played in the team’s achievements, your contribution to the project’s deliverables and how you have supported your manager and the business through the achievement of your objectives.

 

Dig for Information to help you prepare:

Always brush up on your key technical skills before your interview, in case you are asked for technical examples or direct technical questions. However, also enquire about any case studies or written technical tests in addition to your CBI. These are increasingly common, so it is likely that they may come up. If you know you are going to have a CBI, it is also worth trying to get a feel for the competencies that are going to be assessed. Not all companies will divulge this information in advance, however it’s worth asking the question! Your recruitment consultant may also be able to help guide you on typical questions and processes for the interview if they know the company well

 

Be Honest:

You may also be asked for examples of when things didn’t go so well in your current or previous work.  Don’t lie – instead you should always justify honestly why something happened and what you learnt from it. Similarly, if you are asked a question you really don’t know the answer to, it is better to be open about this. Give an opinion on what process you would follow in order to try and answer the question and show your enthusiasm to learn

 

Ask relevant questions:

You are likely to be asked if you have any questions at the end. Show your enthusiasm for the role by asking questions that demonstrate your interest in the role and starting a career with the company. Avoid asking questions that may result in the employer questioning your commitment at this early stage. For example, how many holiday days do I get? What time can I leave in the evening? Do you have showers at the office in case I cycle to work?..... are not the best questions to give a good first impression

 

Salary:

It is advisable not to bring up a discussion around salary during the interview, however the employer may ask you the question directly. In this instance, you should always refer to the fact that the role and company is of more importance than the salary but you should also emphasize you wish to be paid fairly for your skills. You can highlight that you are currently earning X and expect Y (a 10-15% rise maximum is advisable.)

 


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MACHINE LEARNING ENTERS BIOINFORMATICS AND ITS FUTURE IS BRIGHT

Machine Learning Enters Bioinformatics and its Future is Bright

Ever wondered how your email system knows which emails to show you and which to put in your junk or spam folder? Enter Machine Learning. It learns what you open and read and after a time can differentiate what you ignore, toss, or move to spam. Now imagine that same type of learning in the life sciences. As scientific advances move toward Data and Machine Learning to scale their knowledge, you can imagine the possibilities. After all, as you read this, trends in the life sciences, specifically with an eye toward bioinformatics showcase machine learning such as genome sequencing and the evolutionary of tree structures. Human and Machine Learning with a Common Goal There has been so much data provided over the past few decades, no mere mortal could possibly collect and analyze it all. It is beyond the ability of human researchers to effectively examine and process such massive amounts of information without a computer’s help.  So, machines must learn the algorithms and they do so in any number of ways. For the most part, it’s a comparison of what we know, or is already in a databank, with the information we have and don’t yet know. Unrecognized genes are identified by machines taught their function. The Future is Bright Machine Learning is giving other fields within the life sciences both roots and wings.  Imagine scientists being able to gain insight and learn from early detection predictions. This type of knowledge is already in play using neuroimaging techniques for CT and MRI capabilities. This is useful on a number of levels, not the least of which is in brain function; think Alzheimer’s Research, for example.  The hurdle? It isn’t the availability of such vast amounts of data, but the available computing resources. Add to that, humans will be the ones to check and counter-check validity which can in turn become more time-consuming and labor intensive than the computer’s original analysis. And it’s this hurdle which leads to a caveat emptor, or “buyer beware” of sorts. Caveat Emptor: Continue to Question Your Predictions In other words, how much can you trust the discoveries made using Machine Learning techniques in bioinformatics? The answer? Never assume. Always double check. Verify. But as you do so, know this. Work is already in progress for next-generation systems which can assess their own work.  Some discoveries cannot be reproduced. Why? Sometimes it’s more about asking the right question. Currently, a machine might look at two different clusters of data and see that they’re completely different. Rather than state the differences, we’re still working on a system that has the machine asking a different kind of question. You might think of it as a more human question that goes a bit deeper.  Imagine a machine that might say something noting the fact that some of the data is grouped together, but if different, it might say while it sees similarities, but am uncertain about these other groups of data. They’re not quite the same, but they’re close.  Machine Learning is intended to learn from itself, from its users, and from its predictions. Though a branch of statistics and computer science, it isn’t held to following explicit instructions. Like humans, it learns from data albeit at a much faster rate of speed. And its possibilities are only getting started. Want to see where Bioinformatics can take your career? We may have a role for you. If you’re interested in Big Data and Analytics, take a look at our 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 sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

How to lead a Data team

How To Lead A Data Team

Dream teams from sports to business are an ideal everyone aspires to live up to. But what is it every basketball or football dynasty has which makes them a dream team? What is it that brings individuals together to overcome odds, set examples, find solutions, and create the next best thing? Good management.  The need for good management is no different in the Data Science world. Yet according to our latest Salary Guide, poor management is one of the top five reasons Data professionals leave companies. So, let’s take a look at what poor management is, what causes it, and how businesses can better retain Data talent. What’s Your Data Science Strategy? Most businesses know they need a Data team. They may also assume that a Data Scientist who performed well can lead a Data team. But that isn’t necessarily the case. Managers have to know things like P&L statements, how to build a business case, make market assessments, and how to deal with people. And that’s just for a start.  The leader of a Data team has a number of other factors to consider as well such as Data Governance, MDM, compliance, legal issues around the use of algorithms, and the list goes on. At the same time, they also need to be managing their team with trust, authenticity, and candor. The list of responsibilities can be daunting and if someone is given too much too soon and without support, it can be a recipe for disaster. Other businesses might believe that a top performing Data Scientist would make a good manager. Yet these are two different fields. Or you might look at it this way. If you are willing to upskill a top performing Data professional and train them in managerial skills, giving them the education and support they need, that is one solution. Another solution is to create a Data Science strategy which brings in people with business backgrounds. Data Science is a diverse field and people come from a number of backgrounds not just Computer Science or Biostatistics, for example.  Now that you’ve seen what might cause a manager to fail, let’s take a look at a few tips to help you succeed. Seven Tips for Managing a Data Team Managing a team is about being able to hire, retain, and develop great talent. But if the manager has no management training, well, that’s how things tend to fall apart. Here a few tips to consider to help ensure you and your team work together to become the dream team of your organization: Build trust by caring about your team. Help define their role within the organization. Ensure projects are exciting and that they’re not being asked to do project with vague guidelines or unrealistic timeframes.Be open and candid. Remember, Data Scientists are trained in how to gather, collect, and analyze information. If anyone can see right through a façade, it will be these Data professionals. Have those “tough” conversations throughout every stage of the hiring, onboarding, and day-to-day, so that no one is caught unaware.Offer consistent feedback. And ask for it for yourself as well from your team.Ensure your team understands the business goals behind their projects. Let them in on the bigger picture. Think long-term recruitment for a permanent role, not short-term. If you have an urgent project, consider contracting it out. Prioritize diversity to include academic discipline and professional experience. Does the way this person view the world expand the knowledge of your team’s knowledge? Dream teams don’t always have to agree. Sometimes, the best solutions are found when there are other opinions. Finding the perfect, “Full Stack” Data Scientist or Data Engineer or Analyst is not impossible, and retaining them can be even easier. If you’ve done your job well, your team will trust you, have a balanced skillset, and understand how their work supports the organization and its goals. For more information on how to be a great manager, check out this article from HBR.  Ready for the next step?  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 sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

AI and Predictive Analytics Check-in to Hospitality

With summer in full swing, many of us are either planning our vacations, or have already enjoyed them and are thinking of where to go next. Regardless of location, we’re all looking for the same thing; a great experience to remember for years to come. No matter how exciting our trip, we all want our plans to run smoothly and, luckily, AI is here to help.  Today we have more options and more buying power than ever before. The ease with which we can search and select via our phones has kept businesses on their toes and driven them to look beyond traditional service.  By incorporating AI, the hospitality sector is implementing new ways to serve their customers more easily and efficiently. Fueling the AI Hospitality Experience The hospitality industry has a notoriously high turnover rate, relying heavily on seasonal workers, and those early in their careers. But, with AI, digital analysis, and predictive analytics entering the industry, new technologies are providing alternative customer service solutions: Predictive Analysis  Automation  Smart Domotics Advertising Predictive Analysis  As ‘the customer is always right’, the best way to create a smooth and memorable experience is to know what they want and then give it to them.  Given that difficulties can arise when there are too few staff for the number of guests, there is a need to be proactive when planning, in order to be reactive on the day. Utilizing Machine Learning, facilities can predict staffing and supply needs, planning for a more streamlined and, ultimately, better service.  Automation  Automating repetitive operations such as check-ins and check-outs, room assignments, and housekeeping deliveries gives staff more time to focus on the customer. As small and large hospitality businesses compete with the growing success of home sharing platforms, such as AirBnB, AI can give traditional facilities a fresh edge. In addition, rapid and efficient responses lead to greater customer satisfaction which, in turn, leads to a healthier bottom line.  Smart Domotics  More and more hotels are looking to the Internet of Things and Linked Technologies as they evolve into ‘smart’ destinations. With devices that can measure everything from room temperature to customer preferences, facilities can adapt in order to create an optimal environment. Furthermore, interaction with these ‘smart’ technologies can help hotels evolve over time, placing a greater emphasis on elements that prove to be the most popular with customers.  Advertising  From targeted Social Media campaigns to personalized gifts on arrival, Analytics can enhance the entire customer experience. When booking, users can engage with Chatbots 24/7, adding an element of humanity to the online booking experience.  When customers engage with resort apps and website, AI technologies cross-check their interactions and adapt their recommendations accordingly. With more people travelling than ever, the effort of keeping up with travelers the world over, night and day, is shifting to AI, thereby allowing the workforce more freedom to tend to customer needs.  AI in the Cloud The world of digital is transforming our lives, and the rise of Cloud technologies has taken digital analysis to the next level. With the advancements in AI, the hotel industry needs professionals who can create apps, collect and translate data, and, of course, build rigid infrastructures.  If you want to help hotel owners get a leg up on their competition and have a hand in creating a memorable travel experience for someone, we may have a role for you. To learn more, check out our current vacancies.  For the West Coast team call us at (415) 614-4999 or email us at sanfraninfo@harnham.com. For our Mid-West and East Coast Teams call us at (212) 796-6070, or email newyorkinfo@harnham.com.

Why Texas is the place to be for technology jobs

Why Texas is the place to be for technology jobs The big data market is heating up the world over, and perhaps no more so than in Texas. The Dallas, Austin and Houston areas in particular are experiencing a massive boom in big data jobs, with many large tech companies making the move from Silicon Valley to enjoy all that Texas has to offer. But why the shift towards the southern state, and what does it mean for candidates looking big data jobs and broader technology roles? Tax-free Texas The Texan market is looking increasingly lucrative for both young start-ups and established tech companies alike.  One of the most significant factors in this rapid growth is the favourable tax conditions in the state. There’s no corporate or individual income tax, with Texas ranking 47 out of 50 states when it comes to taxes paid per $1,000 of personal income. As California tax rates hitting up to 10.84 for corporations and 12.3% for individuals, it’s understandable that entrepreneurs and big business alike are looking to the southern state for bigger breaks on tax day. On top of this, Texas offers favourable funding and regulatory conditions for young and growing businesses, providing a ‘pro-business’ environment for corporations to thrive. Texas State offers billions of dollars in incentives to businesses every year, providing all the more reasons for those in the technology industry to think hard about making the move. With a state government that celebrates business and provides easy to navigate laws and regulations, many businesses find the transition from Silicon Valley to Austin smooth and seamless. As organisations in San Francisco are priced out of the area, some of the nation’s top talent are moving to pastures greener – and for many, that means Texas. The living is easy On top of the tax breaks gained when moving to Texas, many movers and shakers experience a favourable quality of life. The cost of living is low – for example, the median home value in Austin is $321,600 compared to San Francisco’s $1,1943,300 – with relatively cheap utilities and the second-largest GDP in the nation. The market is robust, which has resulted in money being poured back into cities and communities to make them more attractive to businesses and young families. People can move to Austin and get more bang for their buck than they can in many other parts of the country, enjoying not only a booming technology market, but also superior housing and affordable living. Add in a comfortable climate and famously friendly locals and you’ve got a part of the country that is becoming increasingly appealing to even the most seasoned technology professionals. Technology is taking off Texas is huge when it comes to the technology industry. There was a 41.4% jump in technology industry employment between 2001 and 2013, resulting in large numbers of jobs being taken up across Austin and the wider state. And in 2016 alone, Texas added a huge 11,000 new technology jobs to its market, ranking it second of the 50 states in tech industry employment. The tech hub of Austin alone is home to employers such as Dell, Apple, Microsoft and Samsung, plus an increasingly significant number of start-ups peppering the landscape with innovation. There are a range of incubators and universities that feed into the city’s talent pool, with Austin ranking third in the list of US cities providing the most technology jobs in 2017. However, such growth doesn’t stop Austin and its other Texan counterparts from being a friendly and accessible place to work. There is less of the cut-throat nature that comes with tech in Silicon Valley, and more of a community, collaborative approach. Meanwhile, Dallas-Fort Worth is enjoying being the second-largest data center market in the country, offering an abundance of big data jobs to savvy business people.  Working in Texas Much of the Texas technology market is geared towards candidates currently, with more jobs than skilled employees to fill them. Companies are doing more to attract top talent to Texas, including offering generous benefits packages, relocation allowances and flexible work conditions, and the expectation is that this market will only continue to grow. If you’re looking for technology jobs in Austin or further afield in Texas, we might have just what you’re looking for. Take a look at our US data and technology jobs here.

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