Data Scientist at Blastpoint

Salary Range: $100-140K
Job Description
We’re looking for a thoughtful, curious, and resourceful Data Scientist to join our growing team. You’re comfortable presenting insights to clients, and thrive in situations where you have to learn about and solve a client’s specific problems. This role is the technical face that accompanies our clients’ experience working with us, meaning that you will be the person leading them to solutions by presenting project results, interacting with clients in a consultative manner, and supporting overall customer success.  This is a fully-remote position with the option to work from our home office in Pittsburgh, PA, with occasional company meetings in-person.
Core duties include:

Architect, develop and deploy models and algorithms using customer, open source, and proprietary data; assess model quality, and validate and iterate on those models
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 our platform, and make them ready to sing harmony to a customer dataset’s melody
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 technical bridge between the customer and the product, making our tools useful, relaying product feedback, and customizing to a client’s needs where necessary
Leaning on your empathy and leadership skills, you will work with our clients in a consultative capacity, learning about their particular needs 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.

 
We expect the balance of your work day-to-day to be 60-70% client project-centric work, 20-30% scripting to improve our shared data analytics tooling, and ~10% account management or support needs.  The hope is that feedback from your client experiences can directly make our systems more efficient, and eventually more self-serve, reducing the need for clients to have consultative project teams at all.
 
Professional Requirements

2+ years of experience in data science or educational equivalent; 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 (e.g. K-means, DBSCAN, etc)

Excellent communication skills; comfortable with and effective at delivering 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.
Experience in AWS/GCP/Azure and/or other cloud technologies
Comfort with relational database systems and SQL
Authorized to work in the United States 

 
Preferred Skills and Experience

Expertise in developing data science pipelines, designing and implementing effective monitoring systems for machine learning models in production, and strong knowledge of MLOps
A passion for 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
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 
Some experience with ETL processes or Data Prep tools is helpful
Experience in or exposure to the constraints of a startup environment is helpful
Ability to commute to the Pittsburgh, PA office quarterly, for company-wide meetings, and to travel to client sites when occasionally necessary for a project