For organizations today, a data-driven culture is essential to navigate unpredictable times as competitive, economic and labor pressure mount. Despite the turbulent times, those organizations that invest and take the right steps are more likely to thrive and outshine their competition. One of the most efficient ways in the current market scenario is to stretch the AI and data science prowess further. Companies have to focus on leveraging data, analytics, and technology to drive economic and competitive adjustments. This pressure provides an opportunity to leverage data science talent that has both true data science skills coupled with modern machine learning and related technical skills but that may not be available in-house. As hiring costs and talent shortages continue increasing, companies must rethink their talent acquisition strategies to adapt to changing business environments. While hiring freezes and budget constraints are challenging,hiring seasoned data scientists is expensive, so businesses need to seek alternatives to take advantage of the highly skilled data science professionals spread across the globe.
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In these scenarios, partners like Quantiphi can be helpful. Over 1500+ skilled data science and AWS machine learning professionals are available within Quantiphi. These resources are ready to be outsourced for diverse data analysis and machine learning projects. When leveraging outsourced on-demand talent, customers can quickly onboard resources and then just as quickly scale down based on their needs at any time. These engagements can be short-term or long-term but the outsourced data science professionals eventually become part of a company’s cultural fabric.
Lowering Hiring Costs and Time-to-Hire
Glassdoor estimates the average annual pay for a data scientist is around USD 120,000. With job postings, resumes, interviews, and onboarding, their hiring time is longer than the average time it takes to fill other positions. Hence, outsourcing becomes cost-effective as it eliminates the cost of search and training.
Maximizing Return on Investment
The RoI over hiring is low if a data science expert is needed on a project-by-project basis. Here, utilization and project duration plays a key role.
Data scientists have a wide range of skills and specialties. Finding one person with relevant expertise is complicated. Outsourcing a data scientist gives access to a broader pool of professionals with specific skills and experience.
Suitable for Smaller teams
If you have a small team or a limited budget, outsourcing a data scientist may be a more viable choice.
You can hire an expert for a specific project and release them when it is completed.
Quantiphi’s Data Scientists-as-a-Service (DSaaS) offering is a perfect illustration of how firms can benefit from outsourcing data scientists. DSaaS assists organizations with cost-effective, scalable access to the most skilled data scientists and AWS AI/ML experts. For organizations with a specific data science need or a unique insights initiative, DSaaS augments your team. We offer DSaaS in many flexible ways including:
We have provided complimentary Data Scientists to organizations that already had an in-house team to accelerate and broaden their ability to wrangle, analyze and respond to data inquiries. For example, we helped an investment banking major upgrade its product recommendation model with reinforcement learning. Once the project achieved the necessary SLAs and the services were no longer required, the data scientists were released.
Quantiphi offers access to data science talent with AWS expertise, enabling organizations to leverage the benefits of AWS cloud and machine learning technologies. These professionals are skilled in using a range of tools and techniques for data analysis, modeling, and visualization, as well as deploying machine learning algorithms on AWS. They can help organizations to develop and deploy data-driven solutions on the AWS cloud, optimizing performance, and cost-effectiveness. Quantiphi can outsource data scientists, AWS AI/ML specialists or a combination of both. As mentioned below, each approach offers unique benefits and organizations can choose the one that best suits their needs, allowing them to focus on their core business activities while leveraging the expertise of Quantiphi’s AWS professionals.
1. Outsource Data Scientists – Rather than hiring data scientists in-house, engage Quantiphi talent to perform data science tasks for your organization. This is when you need specialized expertise or want to reduce costs by only paying for your required services.
2. Outsource AWS AI/ML specialists – Hire external individuals or companies with machine learning and artificial intelligence on the Amazon Web Services (AWS) platform to develop and implement AI/ML solutions for your organization. This is when you have the necessary in-house skills and wish to utilize AWS cloud and its machine learning powers to the maximum.
3. Outsource both as hybrid resources – Hire external individuals or companies as a combination of data science and AWS AI/ML expertise. This can be useful if you want to outsource both types of work to a single provider or need a team with a broad range of skills to work on a specific project. It can also streamline the outsourcing process and reduce the number of vendors you need to manage.
To conclude, organizations can access data analytics resources without building an in-house team from scratch or providing extensive acclimatization. Below filters help:
Data scientists with a great mix of technical and business skills and experience in end-to-end model development and deployment are often best suited for the job.
Outsourcing data science talent can provide a significant competitive advantage for organizations seeking to gain insights from their data and drive business success. By leveraging the expertise of outsourced data science talent, organizations can access a wide range of skills and experience without the need to recruit, train, and manage new staff members. This can lead to cost savings, increased efficiency, and improved decision-making.
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