Data Scientist

• Need someone with great forecasting skills
• Need someone who can predict a value in the future that’s not 0 or 1
• Demand forecasting
• Find someone out of the financial sector – stock market, hedge fund, procurement, supply chain
• Dimensionality reduction (what have you done in reducing dimensions?)
• What have you done beyond ‘PCA’ principal component analysis?
• Once we identify a candidate that has done more than PCA – we are cooking!

Month to Month Outlook:

• Will work with hundreds and thousands of rows of data
• Team Size is large (will be a lot of analysts and scientists and their duties are split up). This data scientist will:
• Work on problem type with a consultant
• Come up with Target variable – Time frame
• Set problems up correctly – Predict skews
• Work with analysts on algorithmic assets
• Complete algorithmic assets – Goal is hand off to analysts once completed for future use, etc.
• Strong data science background + machine learning
• Strong experience with scikit-learn (library for machine learning) – need to be fluent in algorithms
• Experience with neural networks + developing in Python
• 2-3 years working experience with industrial business projects applying algorithms
• Building projects from ground up
• Driving process from technical POV
• Explain solutions + understand business process vs data problems

Years’ experience/Degree requirements/ Certifications: 2/3 years’ experience. MS is strongly preferred. Will take bachelor’s Degree if candidates are rock-stars in the above skills section.

Experience & Education Required

• Work with product managers and clients to better understand the business problem
• Think through a business problem and come up with a set of hypotheses
• Create a list of potentially relevant supporting data elements
• Work with data engineers and/or data analysts to procure data and test it for problems
• Collaborate with other Data Scientists
• Propose modeling approaches
• Mine the data to check completeness, value distributions, etc.
• Increment data: data imputation and feature engineering
• Test data for signal
• Tune the model
• Set up tests for the model health
• Collaborate with product managers to find the best way to present the results
• Work with developers on productionizing models - A minimum of 3 years of post-academic experience developing advanced analytics and applying data science in a business environment
• Experience in data mining techniques and methodologies (data prep/modeling, classification, regression, clustering, causal modeling, AI, machine learning, ensemble approaches)
• Experience developing in Python within a production environment
• Advanced experience in data visualization tools with a strong grasp of effective data modeling and visualization practices
• The ability to rapidly develop proof of concepts and test new ideas, as well as the ability to scale these ideas into production ready models that can be deployed within our organization
• Examples of how your passion for data driven decision making and intellectual curiosity translated into value for your organization