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Data Scientist II
Posted 6 days agoViewed
💎 Seniority level: Junior, 2-4 years
📍 Location: Countries around the world
💸 Salary: 149000.0 - 165000.0 USD per year
🔍 Industry: AI
🏢 Company: Invisible Technologies👥 101-250💰 Seed almost 4 years agoInformation ServicesProject ManagementInformation Technology
⏳ Experience: 2-4 years
Requirements:
- 2-4 years of hands-on experience in data science, preferably in a forward-deployed or client-facing role.
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Proficiency in Python or R for data analysis and machine learning, along with strong SQL skills.
- Experience with machine learning frameworks and libraries (scikit-learn, TensorFlow, PyTorch, etc.).
- A proven track record of building and deploying machine learning models in production environments.
- Familiarity with data visualization tools like Tableau, Looker, or Power BI.
- Experience working with cloud platforms such as AWS, Azure, or Google Cloud.
- Excellent problem-solving abilities, with a balance of technical rigor and business insight.
- Strong communication skills with an ability to build relationships and trust with both technical and non-technical stakeholders.
- Adaptable and resourceful, able to thrive in ambiguous, fast-changing environments.
Responsibilities:
- Work directly with clients and internal stakeholders to understand key business challenges and rapidly design data-driven solutions.
- Collect, process, and analyze large datasets from diverse sources, uncovering actionable insights that inform strategy and operations.
- Build, test, and deploy machine learning models that support predictive analytics and optimize decision-making in fast-paced environments.
- Collaborate cross-functionally with engineering, product, marketing, and operations teams to identify opportunities where data can drive impact.
- Communicate complex findings clearly and concisely to both technical and non-technical audiences through reports, dashboards, and presentations.
- Design and run experiments (including A/B tests) to measure and enhance product and business performance.
- Ensure data quality, integrity, and governance across all workflows and deployments.
- Continuously explore new technologies, tools, and methodologies to enhance our data science capabilities.
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