500 word series (2) Formulating the Job Spec
The next step in securing tech talent is to develop the job specification. This involves defining in very clear terms what your organisation requires from the new hire.
Creating a job spec that evokes interest is essential to helping you attract the most suitable candidates for your job. A good job spec can help your jobs stand out from the rest. The key to writing a perfect job specification is finding the balance between providing enough detail so candidates understand the role and your company while keeping your description concise.
The value of a thoughtfully defined job specification will also become apparent during the selection process, when candidates are likely to quiz you in some depth about the specifics of the role. Typically, applicants will want reassurance of your commitment to the role and duties in hand. If you are vague on what you are trying to achieve, or if your appreciation of the scope of the role is absent, it calls into question your credibility as a career destination.
- When creating a job spec, you should start by telling candidates about you. When searching for their next career move, candidates will want to know the history of your company, what you do, your future plans and organisational structure. This should be a brief description that will motivate the candidate to apply for your vacancy.
- You should then start setting out the role objectives. In other words, what specific business problem are you seeking to solve?
- Define the job role parameters. This involves listing the elements required to meet your desired outcomes. Here you can specify what the role will involve, key responsibilities etc. In the case of a data scientist role for instance, this might consist of data collation, data cleansing, enrichment, algorithm development, outcome capture, visualisation, data analysis and results measurement.
- Distinguish between one-off and ongoing business needs. Bear in mind that within most tech roles there will be project work involved, which may have different requirements to the business as usual role. For instance, you may in fact, only require input from an algorithm engineer prior to initial deployment and thereafter on an occasional basis. By contrast, the need for a data scientist with experience in analytics along with a machine learning engineer may be more permanent in nature.
This exercise helps you distinguish between the areas that might be better outsourced to a trusted consultancy. It also helps you to establish the tasks for which there is a clear business case for establishing in-house roles – stopping you wasting time trying to fill positions that are not really needed.
- What experience is required? Candidates will want to know how suitable they are for the role. It is therefore important to consider what skills, experience and qualifications are required. What sort of personality traits should they possess?