Data Scientist/Machine Learning Engineer

Key Responsibilities:

• 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.

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).

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.