Data Scientist

Data Scientist Onsite(Coppell, TX) Direct Hire

Responsibilities

Handle multiple projects at once and act as internal business consultant to help optimize every facet of the organization. You will solve problems and answer questions – using data – for other departments to help reduce costs, reduce errors, and be a better organization. Will understand how the specific task fits into the bigger picture and suggest how to best accomplish it within the project framework. Will self-manage tasks and projects and complete them on time and under general supervision. Every day, you will help solve business problems presented by stakeholders using data. You will get a scenario or problem, such as, “Need to reduce cycle times,” and then pull, analyze, and interpret data relevant to that scenario. You will pull data from SQL databases. You will analyze and interpret data using Python, R, or other tools. You will determine patterns or trends and their statistical significance to the problem. To visualize the data, you will use Tableau. Once you have done your analysis, paired with a data engineer, you will present your findings to the stakeholder and confirm, refute or simply acknowledge a hypothesis. Your work may be implemented or may develop into something else.

Requirements

• To thrive in this role, you must have a solid foundation of statistics and data analysis. You must be able to describe a project where you pulled data, analyzed it, identified trends or patterns, determined significance, and delivered insights or recommendations to the business.

What was your approach? What tools did you use and why? Did you get to the root cause of the issue? Did you confirm or refute a hypothesis? You must be able to speak to your involvement and decisions at each stage. For pulling data, you must be proficient with SQL. To curate and analyze data, languages and programs used are: Python, R, and even Excel. You can choose whatever tool helps you best, but you must be proficient in either Python or R. Excellent communication skills and an attitude of flexibility are a must. Very often, getting into data, some aspect takes longer than thought, or will have to pivot and change how to approach a problem. A dashboard works better than a model, perhaps.