There is widespread need for analytics talent due for several reasons now.
Accumulated data assets, both internal to corporations and external partners and public data sources allow for strategic benefits to corporations. The ability to optimize marketing campaigns, improving customer acquisition, revenue growth, customer focus and customer retention. The ability to iteratively improve product design and new product introduction to meet needs refined and mined through analytics on response data and product usage data. The ability to sense and respond to market shifts in preference, sentiment and use casess as technology and behavior evolve simultaneously. The ability to better understand competitors and their own customer response to those competitive offerings. New tools and technologies, often unknown to the corporations that need to hire specialists, are emerging every month. This gives rise to several sources of complexity and confusion best represented by these hard questions:
- What are the right role definitions for new job requisitions we need to define?
- What aspects of our org chart and culture play best in attracting the right candidates?
- Which aspects of our org change and culture may act as detriments in attacting the right candidates?
- How do we screen those right candidates?
- What are the compensation ranges, and mixture of base, variable and stock that best serve that attracting?
- How do we overcome the data scientist scarcity that makes attracting those candidates so difficult?
- What aspects of the big data scientist destinations (Google, Twitter, Facebook, etc) can we replicate?
- Do we have big data, or medium data, and how does velocity, variety, and value of the data impact our role definitions?
- What are the selection criteria that we should use, specific to our company, culture, needs?
- How much technical depth, statistical depth, domain or industry experience do we really need?
- What kind of time horizon do we need to identify and ultimately select the right candidates, given our criteria and situation?
- Who within our company should interview these candidates, and what are the right questions to focus their decisions during interviews?
- How and where do you find such candidates?
- Is our current recruiting and hiring staff (or agencies) capable of finding and screening the right candidates?
- Is there a way for them to learn how to do this or must we used a specialized agency?
- How do we pick the right specialized recruiting agency, and what are the associated costs and processes?
With these questions in mind, Skyfollow Consulting Group has defined a consulting program to address those needs. Depending on the set of candidates your seek, your time frame, budget and experience with data scientists and data engineers, we can tailor a hiring program for you. In some cases, we can find the specialists you need. In other cases, we work to educate you and create the materials and process you need to go it alone. In yet other cases, we refer you to specialized websites or recruiting agencies that can assist you.
There are five main categories of staff specialty to be aware of:
- Infrastructure engineer: cloud systems, cost, performance, efficiency, and parallel scalability
- Data engineer: defining and creating data sources, data feeds, API usage, and moving right data at the right time.
- Analyst: defining queries, dashboards and reports for management or end users
- Database designer: selecting the right databases, evolving metadata, queries definition and optimization.
- Data scientist: defining statistical and algorithms to find intelligence, results, decisions and outcomes from a variety of data sources.
Please use the contact form below. Indicate your company name, role, questions, and timeframe. Also, if you are candidate in any of these areas, feel free to connect with us to learn more or hear of new selected opportunities.
Contact Skyfollow Consulting Group Here
