Data Scientist/Machine Learning Engineer
Develop, test, and deploy machine learning models to solve business challenges, including forecasting and optimization tasks.
Work with AWS cloud infrastructure to build scalable data pipelines and deploy ML models.
Analyze datasets to extract actionable insights and support decision-making.
Communicate findings effectively to U.S.-based stakeholders in verbal and written English.
Your skills and experience
Requirements:
* Minimum 2 years of experience in data science, machine learning, or a related field.
* Hands-on experience with AWS (e.g., Sagemaker, Lambda, S3, EC2, etc.) for model development and deployment.
* Proficiency in probability and statistics with demonstrated applications in business contexts.
* Strong programming skills in Python (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.).
* English communication skills (verbal and written) for effective collaboration with U.S.-based users.
* Open to remote work within the PST timezone or a hybrid model (onsite at VNT + remote PST).
Bonus Skills (Preferred but Not Required):
* Experience in e-commerce and logistics domains.
* Familiarity with optimization techniques (e.g., graph algorithm) for routing/path-finding problems.
* Knowledge of time-series analysis and forecasting methods (e.g., ARIMA, or deep learning-based approaches).
Why you'll love working here
Benefits:
* Competitive salary ranging from $1,000 to $2,000 per month, based on experience and qualifications.
* Quarterly performance bonuses tied to individual and team achievements.
* Extended health insurance package.
* Opportunities for professional growth and career development in a fast-paced and innovative environment.
* Collaborative and dynamic team culture, where creativity and innovation are encouraged.
* Hands-on experience with real-world business problems and practical solutions in the retail and logistics industries.
* Encouragement of innovation, with the freedom to bring new ideas, challenge the status quo, and help shape the direction of the company’s data strategies.
* Flexible working hours and the ability to work in a remote-first environment, allowing you to balance personal and professional commitments effectively.
* Team-building events and activities, fostering a fun and inclusive company culture.
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