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Company

BlastPoint · 8 hours ago

Junior Data Scientist

Remote - US

Full-time

Remote

Entry Level

$77K/yr - $93K/yr

1+ years exp

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Responsibilities

Develop machine learning models using customer, open-source, and proprietary data; evaluate, validate, and iterate on model performance.

Support the design and automation of our customer data analysis and insights delivery processes.

Contribute to the development of new ML solutions for emerging use cases, client needs, or market demand, and help standardize and templatize these approaches into data products.

Serve as the technical liaison between the customer and the product, ensuring tools are effective, gathering feedback, and customizing solutions based on client requirements.

Utilize empathy and leadership to consult with clients, understand their unique needs, and advocate for them internally and externally. This includes participating in client meetings, leading technical discussions, presenting project outcomes, and engaging in customer-facing technical interactions as necessary.

Dedicate 60-80% of your time to client projects, 20-40% to developing internal tools or data products, and approximately 10% to account management or support tasks. The aim is to leverage client feedback to make our systems more efficient and self-service, reducing the reliance on consultative project teams.

Qualification

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Machine LearningPythonPandasScikit-LearnStatisticsData AnalysisClustering TechniquesAWS Cloud ServicesData Science PipelinesData Visualization

Required

1+ years of experience in data science, machine learning, or predictive analytics; Bachelor's degree or equivalent experience in a relevant field.

Proficiency in Python and essential data science libraries such as Pandas and Scikit-Learn.

Strong foundation in statistics and experience translating business problems into machine learning or predictive analytics solutions.

Demonstrated ability in data exploration and analysis, with the ability to uncover insights and present them through effective storytelling.

Knowledge of machine learning and statistical techniques, including their strengths, limitations, and appropriate use cases; basic familiarity with clustering techniques (e.g., K-means, DBSCAN).

Excellent communication skills, with the ability to present technical information to non-technical audiences.

Ability to collaborate effectively in cross-disciplinary teams.

Self-motivated, organized, and capable of working independently in a fast-paced environment.

A curious and proactive problem solver, open to sharing new ideas, advocating best practices, and self-teaching as needed.

Authorized to work in the United States.

Preferred

Basic knowledge of AWS cloud services, with a particular focus on Amazon SageMaker and its core functionalities for building, training, and deploying machine learning models.

Experience contributing to or developing Python libraries specifically for data science workflows, including packages that enhance data processing, modeling, and analysis.

Proven experience in building data science pipelines, implementing MLOps practices, and monitoring machine learning models in production environments.

Familiarity with time series forecasting, natural language processing, or recommendation systems.

Strong data visualization skills, with experience creating publication-quality visuals for high-visibility reports and a proven ability to convey complex insights from data clearly and effectively.

Willingness to travel domestically for company events (2-4 times per year).