DATA
SCIENTIST
Our client is a fast-growing software-as-a-service (SaaS) company in the eCommerce industry. They are seeking a talented and passionate Data Scientist to join our team and drive the development of data-driven solutions that will shape the future of the industry.
LOCATION
US - Remote
COMPANY TYPE
SAAS Startup
SALARY RANGE
$120,000 - $150,000

What You’ll Do
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Data Analysis: Analyze large datasets to identify trends, patterns, and correlations that can be used to improve our products and services.
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Machine Learning: Develop and implement machine learning models for predictive analytics, recommendation systems, and other data-driven applications.
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Data Visualization: Create compelling data visualizations and dashboards to communicate insights effectively to both technical and non-technical stakeholders.
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Feature Engineering: Identify and engineer relevant features from raw data to improve model performance.
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Model Evaluation: Evaluate the performance of machine learning models and iterate on them to achieve better results.
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Data Cleaning: Prepare and clean data for analysis, ensuring data quality and consistency.
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Collaboration: Collaborate with cross-functional teams, including product managers, engineers, and designers, to integrate data-driven insights into our products and services.
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Research: Stay up-to-date with the latest developments in data science and machine learning and apply innovative approaches to solve complex problems.

Who You are
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Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field (Master's or Ph.D. preferred).
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Proven experience in data analysis and machine learning, preferably in a SaaS or startup environment.
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Proficiency in programming languages such as Python or R.
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Strong knowledge of machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
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Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib).
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Strong problem-solving skills and the ability to work independently.
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Excellent communication skills and the ability to convey complex findings to non-technical stakeholders.
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Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Hadoop, Spark) is a plus.