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How to merge the teams for becoming a AI first company

Which positions need to be filled and what matters.


AI development
is getting easier

Until now, the development of innovative AI technology has required a large number of qualified employees, especially in large companies. Thanks to, individual positions in the development pipeline can either be replaced by functions in the platform itself or the platform can be used in such a way that it makes life much easier for involved developers.

"In an AI First culture, many jobs take on new meanings"


From Lean AI 
to AI First

Processing data and moving from a Lean-AI product to a complete, AI-First product mostly requires an AI-First Team that will work with the Platform. We will recommend you who to hire, where to find them, how to support them, the type of management that works best, and how to structure an organization around them. 

Such people are in high demand, combined to the potential of, and developing a nuanced appreciation of what they do leads to looking for them in places that others ignore, beyond the halls of top computer science schools and into a multitude of disciplines. The challenge down the road is building an AI-First organization. That requires putting people who can build AI throughout an organisation, diffusing knowledge between disciplines. 


You will learn now how to build the team that process data and build models with, covering the different tasks and training required. 

Who to hire

Different technologies require different competencies, and the technology stack utilized by AI First companies and departures is distinct from that of software companies. AI First Companies utilize high volume data infrastructure, high-speed query engines, high-concurrency analytics pipelines, computationally intensive visualization products, ML models and gather feedback. This lesson shows which positions are required by an AI first company and illustrates which of the positions can be taken over by

The competencies required to manage this technology are captured by titles such as data infrastructure engineer, data engineer, and ML researcher. AI first companies without a strong tool like may eventually need a team replete with people in all such roles. 

 Data Analyst set up dashboards, visualise data and interpret model outputs 

Data Scientist set up and run experiments

Data Engineer clean data, create automated data management tools, maintain the data catalogue, consolidate data assets, incorporate new data sources, maintain data pipelines, and setup links to external data sources.

Machine learning Engineer implement, train, monitor, and fix ML models. 

Data product manager incorporate the data needs of the model with the usability intrusions of the product designers and preferences of customers in order to prioritize product features that collect proprietary data.

Machine learning researcher setup and run experiments

Software engineer write the software that delivers the predictions through an interface, application programming interface, API, or other medium.

Designer design the interfaces, including any interactive elements that get feedback data from customers. 

Each of the data and ML-specific roles tend to be filled by people with slightly different educational backgrounds than a traditional software engineer.

 Data Analyst Master of Business Administration (MBA) or bachelor-level courses in statistics, economics, mathematics, and other sciences.

Data Scientist higher level courses in statistics, mathematics, physics

Data Engineer computer science studies with specialization in databases

Machine learning Engineer computer science studies and master level studies in machine learning, mathematics, or physics. 

Data product manager software product management and design management or project management.

Machine learning researcher master- and doctorate-level studies in machine learning, mathematics, physics, or computational neuroscience. 

Data infrastructure engineer higher- level computer science studies with a specialization in distributed systems. 

When to hire

Sequencing hires carefully helps to manage a companies’ capital commitment to its data strategy, ability to absorb lessons, and the cultural impact of going AI-first. The following sequence assumes starting without any data science capabilities and a preference for a smaller initial investment over a larger one. Some companies may have existing capabilities or well-scoped projects that warrant a larger initial investment. AI-First companies, at some point, hire a complete team.

 Data Analyst first, because the business need for improving decision-making informs the prioritization of predictive modelling projects

Data Scientist second, because trying to make predictions with statistical and ML methods provides the initial evidence to support further investment in specific sources of data and types of models.

Data Engineer third, because getting and processing data comes before building models.

Machine learning researcher fourth, because building a model that works in the real world requires robust modeling and integration with existing software. 

Data product manager fifth, because designing a product to get feedback on the models output aids improvement and accumulates proprietary feedback data.

Data infrastructure engineer sixth, because scaling the working models requires managing large volumes of data, processing it fast, and ensuring quality.

Machine learning researcher seventh, because finding solutions to edge cases demands going beyond readily available ML frameworks to push the state of the art forward.

A final note: outsourcing is feasible for some of these roles, depending on the product, team, and systems. Discrete pieces of analytical work, with accompanying datasets, lend themselves to being outsourced to individual data analysts or scientists who operate as independent contractors. This works well when the analysis to perform, or the question to answer, are clear, and the data to find those answers is contained in just a few databases. Further, hiring consultants to set up data pipelines, make sound storage systems can be an effective way to inquire about and instill best practices. The decision to outsource data infrastructure engineering depends on the data and computing infrastructure in use.  

Where to find them

Starting with AI and statistics means hiring analysts and data scientists, engineers and ML researchers. Essentially, be decoupling data science and software engineering, hiring can focus on data scientists even without software engineering experience, this broadening the pool of candidates to include every discipline in which manipulating data is part of the research process. 

One can find analysts and data scientists in the field of economics, econometrics, accounting, actuarial science, biology biostatistics, geology, geostatistics, epidemiology, demographics, engineering, and physics because these areas require high levels of mathematics and statistics. For instance, a potential hire with an extensive background in physics is likely to be proficient at handling the fundamental concepts of data analysis. Within those disciplines, looks for people with skills that enable them to explore datasets in many of the ways already explained. 

Statistics Measurement, process control, instrumentation, and modeling.

Manipulation Modeling, clustering, regression, simulation and visualisation

Operations Optimization, yield management, manufacturing, engineering, systems, dynamics, forecasting, and decision-making.

Learnings Signal processing, control engineering, statistical mechanics, systems, and many more.

How to support them

Ultimately, motivated people- data scientists or software engineers- want to work on meaningful problems. Communicating the problem, articulating the significance of the milestone to leadership, prioritizing work to solve that problem, and getting the solution out to customers motivates data scientists to do good work. Data scientists and ML engineers can´t do much without data, so acquiring well-structured data in a timely manner is the primary way to support an AI-First team. 

Giving right teams right tools like will empower them to do great work. Individual contributors generally have their preferred sets of tools, and new tools come out on a regular basis. There is no universal tool. 

Discover now what the management needs to do to transform the company into an AI first enterprise.

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