freelance data science experts for hire

freelance data science experts for hire

The Rise of Freelance Data Science Experts: Opportunities and Challenges

1. Introduction to the Gig Economy and Data Science

In 2005, Daniel Pink’s book “A Whole New Mind” left many homemakers with an uncomfortable realization. In it, Pink famously argued that Asia was catching up in terms of accounting, law, and engineering, but no one was close in terms of creativity and innovation. So while an automation-fueled economy is all about codifying, standardizing, and routinizing tasks, Pink argued the future of companies, professions, and other businesses would require more than a good idea. It would require optimism to pitch those ideas effectively. Though controversy still remains across the short-term, Pink’s narrative highlights a more important, long-term trend of Freelance Nation.

In recent years, the Gig Economy has become a powerful force. A BCG study shows 150M freelancers in the US, Canada, and Europe, equivalent to about 15% of the workforce. By 2025, it may be one third. Independent workers seem to grow their number today in almost all western and eastern economies. Furthermore, many researchers suggest the workforce trend will have significant macroeconomic effects including a reskilling crisis, a collapse in employee benefits, lower retirement security, and more exploitation risk. Well highlighted by political concerns in some leading economies, this has become a major desideratum of policymakers.

2. Advantages and Benefits of Hiring Freelance Data Science Experts

An increasing number of organizations are eager to tap into data to aid in decision making. Many organizations may use the services of a professional consultancy or enlist the help of a freelance data science expert, either seeking to bridge a gap while recruiting permanent staff or simply due to not requiring permanent in-house staff to fulfill their analytics needs. In addition, the variety of problems to which data science experts are applied is growing. While common cases revolve around the analysis of data and development and implementation of decision models, increasingly companies desire and need to be “data-driven”. Freelance data science experts have the opportunity to be involved in the creation of a myriad of decision support tools, and even the “democratization of data” which could be when tools for automated data entry producing “code”, such as generated natural language essays summarizing large datasets.

Given that it is difficult to impossible to change aspects of work-life balance, which are largely unalterable determinants of life satisfaction, this paper argues that, despite the significant strategic benefits for organizations in adopting data science to inform decision making, when work is identical, the labor market forces acting on an attribute should and will lead people to choose this line of work. This paper uses a new, validated, and scalable assessment tool which can predict an individual’s inclination to follow an analytical career, a broad catchall measure of latent ability, the percentage of data scientist positions advertised requiring a PhD qualification, and salary data to measure the value of a PhD level qualification to discuss the strategic decisions of when to employ and what tasks to assign to freelance data science experts, based on their productivity levels in proportion to value creation, and conclude with guidelines.

3. Challenges and Risks of Engaging Freelancers in Data Science Projects

– Data security and privacy risks: Data security and privacy protection are currently one of the most important risks and biggest challenges in the era of big data, which might prevent the company from adopting an external outsourcing strategy. While focusing on low-level data security such as ensuring data only resides in a certain legal geographic region or data access is only limited within a limited time period, few existing studies have highlighted the high-level privacy considerations which are critical in the practice of outsourcing data processing to external data scientists.

– Importance of protecting intellectual property: Another risk of outsourcing is the leakage of intellectual property through collaborative agreements. While legal instruments might prevent the external recipient from using the intellectual property, they often do not extend to a situation where the intellectual property has been adapted during the process. Since companies rely heavily on data to develop valuable IP assets, the establishment of a strategic IP protection strategy becomes a priority.

– Quality monitoring and control: To guarantee high quality and comply with the strict schedule, companies must have a real-time dashboard that measures the human capital’s quality of outputs: the consistency of analysis such as frequency of code deployment, data references, script annotation; the effectiveness of the scripts such as predictive accuracy, error percentages, and running time; the impact of the work: did it meet the business objectives? At the same time, the evaluation feedback must be obtained anonymously to prevent the potential auto-discrediting effect of employee rating.

– Vendor malfeasance: The risk of non-profitable and even potentially fraudulent strategies by the external data scientists is especially high if the contracted workers have no reputation in the market and can be easily substituted with another cheap one. With the increasing willingness of present digital competitors to cheat, data security, privacy, and IP protection face difficulties to be solved before hiring an external data expert.

4. Best Practices for Hiring and Managing Freelance Data Science Experts

In this paper, we considered the hiring process for individuals. However, in reality, companies often work with teams of freelance data scientists. In our experience, it is key to build deep relationships with freelancers so that they both respect and trust the judgment of the company. Data scientists have different strengths and it is important for everyone to be open to give and receive feedback for a project to be successful. The freelancers are providing a service, but the companies are recruiting freelancers often for their deep expertise in domains that are important to the company. In general, the saying that if you pay people peanuts, you will get monkeys is a good guiding principle for any endeavor.

A more recent trend in companies is to directly hire remote individuals as employees. This may work better if long-term relationships are desired. The remote freelancers, after all, are remote employees. The freelancer hiring model works best if a group of people are interviewing and managing a data scientist. If the freelancers are treated as second-class citizens, they will take their expertise elsewhere. Adherence to best practices for recruiting, performance review, and compensation is key. Hired data scientists may also become financially independent by performing freelance projects, but if they do, then the companies need to show respect by also hiring them using best practices. Such respect also leads to good word of mouth and, ultimately, is good business.

5. Future Trends and Implications for the Data Science Industry

Data science requires a specialist, interdisciplinary skill set that enables the analysis of large sets of data, particularly unstructured data. The demand for individuals with these skills has been estimated to exceed the supply, with one report predicting a shortfall of 85,000 deep analytical talent positions in 2021 in the United States alone. This review detailed the rise of freelance and open source contributors with deep analytical talent, specifically in data science and machine learning, identifying the rise in data science and machine learning influencers, including practitioners engaged in open source projects and competitions, freelancers addressing real-world problems, full-time data scientist roles, experts providing advice, and mentors.

While some established communities have generated their talent base, like the R Consortium with governance in place, other open-source projects appear to be led through less formal, distributed organizations, suggesting the rise of a gig economy or gig organizations, in which temporary positions are common and organizations contract independent workers for short-term engagements. Decision-makers should be critically aware of the potential for inequity in gig work, particularly in data science and machine learning, where widespread skills shortages and deep inequality exist within the industry due to gender, race, and socio-economic status, potentially leading to unlevel playing fields for those seeking a career. These challenges are parallel to those identified by others but not extensively reported. To address the dichotomy, this review includes comments from those in influential roles who have a successful track record by being engaged in open source.