Data Scientist (Machine Learning)

Vintti is revolutionizing remote staffing by prioritizing time zone alignment. We connect US-based SMBs, startups, and firms with Latin American professionals who work synchronously with US schedules. This approach ensures that businesses can maintain their usual workflows, conduct real-time meetings, and collaborate effectively without the typical challenges of working across disparate time zones.

A Data Scientist (Machine Learning) leverages their deep understanding of data analysis, statistical methods, and machine learning algorithms to extract meaningful insights from vast amounts of data. They develop predictive models and machine learning solutions to drive strategic decision-making and optimize business processes. Collaborating closely with cross-functional teams, they apply advanced techniques to uncover trends, identify patterns, and solve complex problems. Their work ensures that organizations can make data-driven decisions, enhancing operational efficiency and fostering innovation in products and services.

- Advanced degree (Master's or Ph.D.) in Computer Science, Statistics, Mathematics, Engineering, or related field
- Strong proficiency in programming languages such as Python or R
- Experience with machine learning frameworks and libraries such as TensorFlow, Keras, PyTorch, or Scikit-learn
- Proficiency in SQL and experience with databases
- Solid understanding of statistical analysis and techniques
- Experience in building and deploying machine learning models and algorithms
- Familiarity with data preprocessing, feature engineering, and data cleaning techniques
- Experience with big data tools and technologies such as Hadoop, Spark, or Apache Flink
- Strong problem-solving and analytical skills
- Familiarity with cloud platforms such as AWS, GCP, or Azure
- Experience with data visualization tools like Tableau, PowerBI, or similar
- Excellent communication and collaboration skills
- Ability to communicate complex technical concepts to non-technical stakeholders
- Experience with version control systems such as Git
- Strong background in mathematics, probability, and statistics
- Understanding of data structures, algorithms, and software engineering principles
- Familiarity with data pipeline orchestration tools (e.g., Apache Airflow, Luigi)
- Prior experience working in a collaborative, team-based environment
- Attention to detail and commitment to high-quality, error-free work
- Strong desire to stay updated on emerging technology trends in machine learning and artificial intelligence
- Experience mentoring junior team members, providing constructive feedback and guidance

- Design and implement machine learning algorithms and models
- Analyze large, complex data sets to derive actionable insights
- Develop and optimize prediction and classification systems
- Customize and apply machine learning techniques to significant volumes of structured and unstructured data
- Collaborate with cross-functional teams to understand business requirements and translate them into data specifications
- Conduct data cleaning, processing, and transformation
- Perform exploratory data analysis (EDA) to identify trends and patterns
- Validate and evaluate the performance of machine learning models
- Document models and methods to ensure reproducibility and maintainability
- Communicate findings and recommendations to stakeholders through reports and presentations
- Continuously monitor and refine machine learning models to improve their performance
- Research and stay updated on the latest advancements in machine learning and artificial intelligence
- Implement data pipelines and workflows using tools such as Apache Airflow or similar
- Troubleshoot and debug issues with models and data pipelines
- Use data visualization tools to create dashboards and reports
- Work closely with data engineers to manage data resources and ensure data quality
- Experiment with different algorithms, features, and parameters to maximize model performance
- Gather and process raw data to prepare it for analysis and modeling
- Participate in code reviews and contribute to maintaining a collaborative development environment
- Mentor junior data scientists and provide guidance on best practices

The ideal candidate for the Data Scientist (Machine Learning) role is a highly-skilled and motivated individual with an advanced degree in Computer Science, Statistics, Mathematics, Engineering, or a related discipline. They possess strong proficiency in programming languages such as Python or R, complemented by extensive experience with machine learning frameworks and libraries like TensorFlow, Keras, PyTorch, or Scikit-learn. Demonstrating a solid understanding of statistical analysis, they have a proven track record of building and deploying effective machine learning models and algorithms. The candidate is adept at SQL and familiar with big data tools and technologies such as Hadoop, Spark, or Apache Flink, and they are proficient in using data preprocessing, feature engineering, and data cleaning techniques. Their analytical mindset and exceptional problem-solving skills enable them to derive actionable insights from complex data sets. They excel in cloud environments such as AWS, GCP, or Azure, and their experience with data visualization tools like Tableau or PowerBI allows them to communicate findings clearly to both technical and non-technical stakeholders. This individual thrives in collaborative, fast-paced settings and consistently demonstrates keen attention to detail and a commitment to high-quality, error-free work. Strong communication skills, the ability to mentor junior team members, and a proactive, adaptive attitude further distinguish them as an ideal candidate. They possess a genuine passion for continuous learning, staying at the forefront of emerging trends in machine learning and artificial intelligence, and exhibit strong leadership capabilities and exemplary time management skills.

- Design and implement machine learning algorithms and models
- Analyze large, complex data sets to derive actionable insights
- Develop and optimize prediction and classification systems
- Customize and apply machine learning techniques to significant volumes of structured and unstructured data
- Collaborate with cross-functional teams to understand business requirements and translate them into data specifications
- Conduct data cleaning, processing, and transformation
- Perform exploratory data analysis (EDA) to identify trends and patterns
- Validate and evaluate the performance of machine learning models
- Document models and methods to ensure reproducibility and maintainability
- Communicate findings and recommendations to stakeholders through reports and presentations
- Continuously monitor and refine machine learning models to improve their performance
- Research and stay updated on the latest advancements in machine learning and artificial intelligence
- Implement data pipelines and workflows using tools such as Apache Airflow or similar
- Troubleshoot and debug issues with models and data pipelines
- Use data visualization tools to create dashboards and reports
- Work closely with data engineers to manage data resources and ensure data quality
- Experiment with different algorithms, features, and parameters to maximize model performance
- Gather and process raw data to prepare it for analysis and modeling
- Participate in code reviews and contribute to maintaining a collaborative development environment
- Mentor junior data scientists and provide guidance on best practices

- Analytical mindset with strong problem-solving abilities
- Keen attention to detail and accuracy
- Ability to work independently and manage multiple tasks
- Strong communication skills for translating complex data insights to diverse stakeholders
- Collaborative team player with a proactive attitude
- High level of curiosity and enthusiasm for technological advancements
- Adaptive and flexible in a fast-paced environment
- Strong time management skills and ability to meet deadlines
- Creative thinker with innovative approaches to problem solving
- Consistent commitment to high-quality, error-free work
- Passion for continuous learning and personal development
- Demonstrated leadership capabilities and mentoring skills

- Competitive salary based on experience and market standards
- Comprehensive health insurance (medical, dental, vision)
- Retirement savings plan (401(k) or equivalent) with company match
- Paid time off (vacation, sick leave, personal days)
- Flexible working hours and remote work options
- Professional development opportunities, including conferences and workshops
- Continuing education reimbursement for relevant courses and certifications
- Access to online learning platforms for skill enhancement
- Opportunities for career advancement within the company
- Generous parental leave policy
- Wellness programs and access to fitness facilities
- Company-sponsored social events and team-building activities
- Employee assistance program (EAP) for mental health and counseling services
- Relocation assistance for eligible employees
- Annual performance bonuses and merit-based raises
- Stock options or equity shares for eligible employees
- Transportation benefits or commuter assistance
- Ergonomic office equipment and standing desks
- Regular recognition and reward programs for outstanding work
- Collaborative and inclusive work environment with a focus on diversity and inclusion