How To Get The Best Out Of Your Freelance Data Scientists
With the advent of remote working culture, employers are getting comfortable in hiring freelance data scientists instead of creating a full-time core data science team. Not only does it provide a flexible work schedule for data science freelancers but also offers a lot of benefits for organisations, such as the best return on investment for companies, especially small businesses, and the hiring is quicker as well as less expensive. In fact, in a report, it has been stated that there are 15 million freelancers in this country, across the industry, which is expected to double up by 2023, including data scientists. This growth can be a result of the economic downturn due to the COVID-19 pandemic, which is forcing many professionals to start freelancing to stay relevant in the industry. However, for an organisation, managing and getting the best return on investment from your data science freelancers is indeed a challenging task. Considering the freelancers work on their schedules, as well as have different working habits, it gets complex for employers to have control over their freelancers virtually. Nevertheless, there are a few ways that can help organisations get their projects done smoothly via freelance data scientists. Understanding The Requirement Of Hiring Freelance Data Scientists Before actually managing data science freelancers, businesses need to understand their requirement for a freelancer instead of hiring full-time developers. Freelancer data scientists come with a lot of benefits such as — less expensive, as they work from home, the pay for freelancers are indeed less than full-time data science employees; they are less hassle and can be hired for a short period of time or on a project basis, which in turn can help organisations to spend less on data science skills. However, freelance data scientists also come up with a few disadvantages considering they are not placed inside the office; it gets difficult for employees to manage the pool of talents and their work productivity. Also, these freelancers work remotely, and therefore there could be a lag in response time, which can hamper businesses bottom-line. Freelance data scientists also come with security risks, as they are not working under a secured network of the offices, many of these freelance devices can create vulnerability for sensitive business information. Understanding these aspects and making necessary changes can help employers manage their freelancers better. Maintaining A Budget For Hiring Freelance Data Scientists Another important aspect of hiring and managing freelance data scientists is to create a strict budget for freelancers and organise your rest of the expenses to stick through it. Keeping a budget for hiring freelancers is equally essential for businesses, as deciding other budgets for earning effective profits. By defining the budget, companies will have a better opportunity to understand the costs involved as well as can find better ways to optimise it. Although freelance data scientists are less expensive to hire, managing a pool of those freelancers requires businesses to create a better budget strategy. Also, as businesses keep hiring freelancers, maintaining a budget can provide necessary information as to the amount of value a freelance data scientist can contribute to your organisation at that time. Finding Relevant Talent For Your Organisation Considering the market for freelancers is expanding with platforms like — Upwork, Total, Data Science Central, Data Science Stack Exchange — providing several options for organisations to hire freelance data scientists, it gets challenging to find the right freelancer for your business. Businesses should know their requirements and hire relevant experts to get the most out of them. Not all data related jobs require data scientists. In fact, there are other experts like data engineers, data analysts, data architects, or a statistician who can help businesses make an informed data-driven decision. And therefore, it is imperative for employers to understand their requirements and their business problems before hiring data science freelancers. Companies should also ensure that the hired freelancers have relevant experience and are skilled with necessary tools to supplement their businesses, such as Google Cloud ML Engine, Amazon SageMaker, Apache Spark, Jupyter Notebook, to name a few. Defining Business Problems The most critical step of getting the most out of your freelance data scientists is to provide well-defined project details for them to understand your business problems, which will help them design a solution personalised to your business. In order to make freelance data scientists understand what needs to be done from their side, businesses need to clearly communicate business problems with them. Employers should also state the time frame to their freelancers, which will help them define their goals and manage the time accordingly. Considering freelance data scientists work with several organisations simultaneously, a defined deadline will help them manage their other projects and reduce the time lag for important deadline based projects. Clear definition of project details will provide effective communication with your data science freelancers, which, in turn, will help in project success. Tracking Productivity Of Freelance Data Scientists Similar to your full-time data science team, it is also crucial for businesses to track the productivity of your freelancing team on a real-time basis. Businesses should be aware of the amount of time that is being spent on each project by their data science freelancers, which will provide businesses with a better view of their freelancers as well as can understand their value to your organisation. Thus, monitoring will also show business leaders the amount of time left to meet their deadlines. To track and monitor the productivity of your freelance employees, businesses also use several project management tools, which provide a single view dashboard of your freelance data scientists. These freelancers’ management tools also help businesses in generating reports that can help during the time of getting the next batch of remote workers. Alongside these tools also help in creating to-do lists and assign tasks for your freelance data scientists, as well as can help in keeping track of business work-related details. Some of the popular project management tools are — Trello, Jira, ClickUp, Basecamp, to name a few. Build An Effective Communication Channel For Your Data Science Freelancers Another important aspect of managing your set of freelance data scientists is to have an effective communication with the team. Considering the freelancers work remotely from different parts of the region, it is crucial for businesses to have two-way communication to ensure smoother management. And in order to have such an active communication business need to take help from some of the popular communication tools that have gained traction amid this crisis — Skype, Slack, MS Teams, WebEx etc. In fact, in a recent survey done by Analytics India Magazine, it has been revealed that businesses are using collaborative tools in order to have smooth communication with their remote working team. These tools will help companies to keep a constant touch with their freelance data science team, which will provide them with constant guidance to improve their workflow, as well as manage their time better. With the help of these tools, freelancers can also clear their day-to-day doubts and concerns related to their project, which will provide a better outcome for the business.
With the advent of remote working culture, employers are getting comfortable in hiring freelance data scientists instead of creating a full-time core data science team. Not only does it provide a flexible work schedule for data science freelancers but also offers a lot of benefits for organisations, such as the best return on investment for […]
Nevertheless, there are a few ways that can help organisations get their projects done smoothly via freelance data scientists.
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["AI", "ML", "data science", "analytics", "Amazon SageMaker", "Jupyter", "Apache Spark", "R", "Go", "GAN"]
Sejuti currently works as Associate Editor at Analytics India Magazine (AIM). Reach out at sejuti.das@analyticsindiamag.com