What are the top skills for a successful algorithm designer?

In my opinion the capability to recognize that a certain problem can be seen as an OR- problem is one of the most important requirements. A lot of perplex don't recognize it and solve such problems by gut feeling recycling third best solutions. Once recognizing an OR- problem, the rest is merely effort.

Establishing axioms to prove theorems in optimization, especially in convex space, is an essential first step in any optimization algorithm.

Notice that the most optimum solution is not necessarily the best solution for the business; some times a less optimum but a more stable solution is a better option. Business requirements changes; your solution needs to be easily modified to address the ever changing business requirements. Think long term; often times the most optimum solutions performs worse than the second optimum solution with slight changes in biz requirements.

There is a strong dependency on every step with its previous one in problem analysis. Wrong assumptions are likely to yield ((guess what)) wrong models, which can output solutions that are far from expectations. Specialists in OR are key in understanding the system where the problem is defined before formulating any algorithm. That is, a well-defined statement of the problem of interest is a must-have before any action.

In my view following are the top skills required for a successful algorithm designer (Definition: A Professional who either builds/creates or utilizes mathematical and computing tools to solve problems) 1. Solid understanding (theory and efficient implementation) of the existing algorithms 2. Business acumen 3. Creative thinking 4. Problem solving skills 5. Proficiency in technologies (coding ops) 6. Compelling story telling skills

In my experience, the simplest formulation of a Mathematical model is to consider it into a triangle of three aspects: 1. The governing equation : This is the key differential, or linear equation, call it the engine of the Model. It defines the behavior of your analysed problem, giving order of response to a particular input. More of like how much volume of water flows out per diameter of nozzle opened.. 2.The natural boundary equation: This defines the resistances of the atmosphere in which your governing equation operates. More of like Air resistance that the water experiences as it outs from the nozzle 3.The Essential boundary conditions: These are the initial nonvariable data points. More of like initial pressure, viscosity of water

Data Structures: Products: - Variables: Pi for each product i. - Properties: Name, quantity, price and supplier information. Transactions: - Variables: Tj for each transaction j. - Properties: Type (sale or restock), product involved, quantity and timestamp. Functions: Update Function:(Pi,Δ) update(Pi,Δq) - Updates the quantity of product Pi by Δq (positive for restocking, negative for sales). Sales Report Function:() generate Sales Report(start date, end date) - Returns a report of sales between the given dates.

End to end seam fit..team fit..line manager satisfiability are some real world examples in the line between academic world and work place. Technical line of work required rigorous research or at times can be relaxed when faced with a wall of impenetrabilty.

"Coding and debugging" represents the expertise in translating conceptual algorithms into functional code, ensuring it is efficient, readable, and maintainable. Debugging is the process of diagnosing and correcting code errors, a critical skill for guaranteeing that the algorithm performs reliably under various conditions. Proficiency in these areas is vital for algorithm designers to produce robust and effective software.

To easily identify and fix errors, I find it helpful to refactor my code into functions. In predictive analytics where a typical code is required to clean missing data, scale, split and train the dataset, I would create a function for each of these components to ensure ease of tracing any source of error during the debugging step before model deployment.

This is usually backed up by hand calculations or experimental data from the field. These serve to give a baseline/guideline for expected correlation between your inputs and outputs. So it’s key to always have data to lean against.Your hand calculations might not be as complex as the mathematical model developed, but should reflect a possible correlation similar to your algorithm model.

When you get that there is problem than its analysis is something really mandatory. Most of the people think that analysis need to be done only for the case where results are there but they need to be double-checked in the case when any issue arised

Communication and documentation is some thing that is mandatory and specifically required for any company as it not only helps to understand the problem that need to be fixed among teams but it also helps to fix the need of documentation evidence that is specifically required

Keep in mind that there already may be packages that implement the algorithms that you need. Don't reinvent the wheel when it isn't necessary.

Your first step should be always literature review; what has already been done?! The next step should be arguing why the available solutions does not meet your needs. Only then start to design a new algorithm or tweak an existing one; Remember, each hour spent on reinventing a wheel is two hours lost; one hour spending on reinventing the wheel; one hour that you could have done something useful for the project but you missed.

Top skills for a successful algorithm designer include algorithmic thinking, proficiency in data structures, strong problem-solving abilities, programming expertise, analysis of algorithm complexity, mathematical aptitude, domain knowledge, optimization techniques, critical thinking, and a commitment to continuous learning. These skills collectively empower designers to conceptualize and solve problems efficiently, navigate data structures effectively, and optimize algorithms for real-world applications, showcasing a versatile and adaptive approach to algorithm design.

An algorithm designer ensures that a process that involves creating a step-by-step procedure or set of instructions for a computer to follow while performing a task or solving a problem is effective and efficiently put in place. The procedure involves integrating a series of steps, including the following skills problem analysis, requirements gathering, algorithm design, and implementation