データサイエンス is one of the most frequently hired jobs right now, with the need for an expert data scientist spanning a wide range of industries and domains. The emergence of a ギグ・エコノミー of industry experts has also seen expert data scientists provide on-demand services on フリーランス・プラットフォーム, offering companies a convenient way to access them for short-term consulting projects.
Platforms such as Kolabtree, for instance, have a curated list of over 20,000 独立専門家 available for niche consultations. These experts guarantee complete データセキュリティ and confidentiality and can be hired without any minimum contract requirements.
If you’re thinking of hiring a data scientist for your organization, read on to understand the things you need to know before hiring.
Who is a data scientist?
Data scientists are experts in data management, analysis, and interpretation. They make use of various tools, in order to make sense of data and help organizations make better decisions. They provide insights into a phenomenon or an event, by ascertaining patterns and trends in data collected, finding themselves predominantly occupied formulating questions about the data.
This creation of algorithms and data models helps them to forecast outcomes and understand what the data is trying to convey. Data scientists receive immense support from data analysts, who cleanse, validate and process data to help them achieve these goals.
Data Scientists also work closely with senior management to understand business goals, and to determine how data can be utilized to best attain those goals. With the advent of technology, our world has become data-driven i.e. data is pervasive in every field. As a result, data science has evolved into a vast field and every sector has niche expertise in data science requirements.
Depending on your business model, sector, project, timelines, etc., your data engineering and analysis needs might vary. Therefore, it will be helpful to understand the different types of data scientists available, the skills they have, and the work they perform:
- 機械学習 データサイエンティスト play around with varied algorithms to develop programs tailored for suggesting pricing strategies and products best suited for your firm. They also use machine learning to derive patterns and forecast demand.
- Statisticians apply their key skills of confidence intervals and data visualization, to help you achieve business goals such as profit, inventory optimization, cost-benefit analysis, etc.
- Actuarial scientists have a great grasp on mathematics そして statistics, which they use for データ分析 that helps to measure and manage the outcomes. They are highly valuable for financial institutions, such as banks and insurance companies.
- 数学者 have profound knowledge of applied mathematics and operational research which are highly sought after by businesses to execute optimization and analytics in several fields, such as inventory management, supply chain, pricing algorithms, etc.
- Data engineers are the custodians of data for a company. They design, build and manage the data received and collected by an organization. They analyze and process data in line with an organization’s requirements.
- Software programming analysts perform calculations using programming. They have expertise in computer languages such as python and r programming. They also possess data visualization and analytics skills as well.
- Digital and Big Data Analytics Consultants have technical talent, as well as sound business and marketing skills. They can configure web pages to collect data and direct it to analytics tools. They can further finally visualize it through filtering, processing, and designing.
- Business Analytic Practitioners have profound knowledge of both – business and data analysis. They work on crucial decision-making processes like dashboard design, ROI analysis, high-level database design, ROI optimization, etc.
- Spatial Data scientists make use of spatial data collected via Google maps, bing maps, car navigation systems, and several other applications for optimization of navigation, localization, site selection, etc.
- Quality Analysts use statistical processes and modern analytic tools to prepare interactive visualizations which serve as core inputs in decision-making processes for several business functions like finance, management, sales, human resource development, and marketing.
The Need for Hiring a Data Scientist
With data becoming a prerequisite to any company’s growth, hiring an efficient data scientist can help you study patterns and make strategic decisions in line with market trends.
The real question, though, is how you can figure out the exact type of data scientist you would need. With hybrid teams becoming popular these days, a smart financial move would also be to identify whether to hire them as a freelancer or as full time employees.
If you are a small firm in particular, chances are that you do not have enough capital to dedicate it to an in-house data scientist and, thus, hiring a freelancer will be prudent. Platforms like Kolabtree, for instance, are excellent places to find freelance data scientists on flexible contracts.
を採用しています。 フリーランスのデータサイエンティスト is not only budget-friendly but also incredibly efficient, as they bring expertise and insights from working in different firms and sectors. It’s also a cost-effective practice, with these freelance experts available for on-demand consultations as and when you need them.
If you’re thinking of hiring a data scientist, there are several important factors to consider.
Project Description, Budgets and Scope
First, make sure you define the job description clearly. Include important information regarding the organization’s need for the data scientist, project details, and skills required. Sometimes firms refrain from providing details of the role and project and dwell into only technical skills required, which may not lead to right candidates applying for the role.
Giving out a detailed JD will go a long way towards ensuring that you receive proposals and resumes of candidates who will potentially be right fit not only for the role, but also for the company. Providing additional information, such as the scope and duration of the project, will ensure that you attract proposals from candidates who match your schedule and work timeline.
Finally, budgets are a vital part of the hiring process. Mentioning an approximate estimate of the money you’re willing to spend on the hire will help you limit your pool of potential hires to データサイエンスの専門家 that fit your budget. This will help you avoid back and forth, and focus your effort on evaluating candidates that fit into your pay scale.
Typically, a data consultant charges an average of $200 to $350 per hour. In-house data scientists’ salaries range from $54,000 to $140,000 per year, with the average salary being $94,000. Freelance data science experts, however, are much more cost-effective, offering on-demand consultations from $50/hour up to $130/hour, depending on the project scope and skills needed..
Posting a project and evaluating experts
Once you have a concise and clear job description, the next step is to decide where to post it. Freelance platforms like Kolabtree, for instance, provide 独立専門家 from across the globe that you can scout and hire. These online platforms take special care to vet these profiles before they sign up the platform, ensuring that you only contact top-rated experts in a secure ecosystem.
After you have successfully gotten a pool of eligible candidates for the job, the next step is to filter out the one best suited for the company. Conducting a small test will help you find the right talent. You might want to test their skills in computation, problem-solving, and application of technical knowledge.
Say, for instance, you want them to come up with a prediction for new flu viruses to design efficient vaccines each year. You can give this test in the form of a take-home assignment, quiz, technical round with a question-and-answer session, or in an interview for this task to be performed. You can also ask them other relevant technical questions to examine their hard skills, but asking questions to test their soft skills and behavioral traits is equally important.
After all, data scientists work closely with business management and various stakeholders of business functions. This mandates that their communication and people management skills must be top-notch to understand the requirements of the firm and produce efficient and feasible solutions.
You can ask them about challenges they have faced, professionals they admire, past projects they have worked on and their contribution to the team, etc., which will help you to assess their mindset and ascertain whether they will align to the work culture your firm thrives upon. These strategies should help you find a data scientist with the right blend of technical and people skills.