Data Scientist

Pittsburgh, Pennsylvania

Data Science

130000

Permanent

Our client are a B2B data analytics Startup that give companies the power to solve business problems through discovering the consumers in their data and understanding how they think.

They are looking for an insightful, introspective, and inquisitive Data Scientist to join thier growing team where you will be presenting insights to customers and thrive in situations where you learn about and solve a customer specific problems.

This is a complete remote position with the option to work from their home office.

Responsibilities:

Build, develop, and deploy models and algorithms using customer, open source, and proprietary data; assess model quality, and validate and iterate on those models
You will own the process of integrating customer data, analyzing it using our methodology and your data instincts, and make it deliver value to the customer
Evaluate the effectiveness and accuracy of public and private data sources, choose the right ones for their platform.
Help design and automate our customer dataset analysis and insights delivery process, to smoothly handle a wider variety and higher velocity of data
Act as the liaison between the customer and the product, making our tools useful, relaying product feedback, and customizing to a customer as needed.
Leaning on your empathy and leadership skills, you will work with our clients in a consultative capacity, learning about their specific necessities and being their advocate both internally and externally. That means participating in client meetings, leading technical discussions, presenting project results, interacting with clients in a consultative manner, and other technical customer-facing engagement, as needed.

Professional Requirements

2+ years of experience in data science; strong preference for additional experience in software, R&D, consulting or adjacent fields.
Bachelor’s degree or equivalent experience in computer science, mathematics, statistics, economics, or similar.
Understanding of cluster-analysis techniques (K-means, DBSCAN, etc).
Excellent communication skills; comfortability with presenting presentations.
Comfortable with Python and common accompanying tools including Pandas and Scikit-Learn.
Deep understanding of statistics and other mindsets for building models from data; strong data acumen in translating business problems into supervised/unsupervised machine learning problems.
Comfortability with relational database systems and SQL.
Authorization to work in the US.

Preferred Skills and Experience

Passionate about discovering the secrets and solutions hidden in large datasets.
Highly attentive to detail, with a skeptical sixth sense about signal-vs-noise.
Ability to self-motivate, self-organize and work independently in a challenging, fast-paced environment with several ongoing concurrent projects.
A willingness and demonstrated ability to work collaboratively with a small team; excellent internal communication skills.
A can-do mentality, with the willingness to roll up your sleeves and take initiative to solve something when necessary.
Knowledge of a wide variety of machine learning and statistical analysis techniques, their advantages and drawbacks, and areas of best applicability.
Experience in designing and implementing effective monitoring systems for machine learning models in production.
Familiarity with machine learning approaches for time series forecasting and natural language processing (NLP).
Curious and eager problem solver, willing to bring new ideas to the team and advocate for best practices, and able to self-teach new skills when needed, with a hunger for building well-designed, high-quality solutions
Recognition that there are always multiple answers to a problem and the ability to engage in a constructive dialogue to find the best path forward.
A bit of experience with ETL processes or Data Prep tools is useful.
Experience in a Startup environment is helpful.
Ability to commute to the office quarterly, for all-staff meetings, and to travel to customer sites when necessary for a project.