Senior Data Scientist

About the role

As the Senior Data Scientist at Going, you will play a pivotal role in shaping the future of travel exploration and recommendations. Leveraging your expertise in data science and machine learning, you will spearhead the development and implementation of advanced algorithms and models that deliver personalized and compelling travel recommendations tailored to individual travel goals. Working closely with cross-functional teams, you will translate business requirements into innovative data science solutions, from feature engineering and model development to optimization and deployment. Your strategic leadership will shape the future of our data science capabilities, driving continuous innovation and enhancing the user experience. If you're passionate about utilizing data to unlock new possibilities in the travel industry and thrive in a dynamic environment that blends both autonomy with support , we invite you to join us on our mission to redefine travel discovery for millions of users worldwide.

Due to the high volume of applicants we expect to receive for this role, we will be closing applications 4/19/24 to ensure we can review and respond to all candidates that have applied.

In the short term, you will

• Understand the short and long-term vision of the company. In collaboration with Product and Engineering leadership, define the roadmap for advancing our data science capabilities in travel recommendation and ensure alignment with product development timelines.

• Work closely with product managers, designers, and business stakeholders to understand user needs and business requirements and translate them into actionable data science solutions.

• Collaborate with data engineers to design and optimize data pipelines for efficient model training and inference.

• Utilize large-scale datasets to extract meaningful insights and identify patterns that drive actionable, delightful recommendations for users.

• Evaluate the technology needs for the data science domain and actively advocate for scalable solutions where necessary.

In the long term, you will

• Implement the data science domain strategy that aligns with the overall vision by organizing and actioning tactical initiatives to deliver the vision.

• Lead end-to-end development of machine learning models and algorithms for travel discovery, from data collection and preprocessing to model training, evaluation, and deployment.

• Explore novel approaches in collaborative filtering, content-based filtering, and hybrid models to enhance recommendation quality and diversity.

• Engineer and refine features from diverse datasets, ensuring models leverage user interactions, historical data, and relevant external factors for accurate predictions.

• Optimize models for speed and efficiency, continuously iterating to adapt to evolving user preferences, market trends, and business dynamics.

• Implement an experiments and A/B testing framework to enable opportunity discovery by product managers.

• Spearhead model evaluation and improvement cycles using robust metrics, experimentation, and A/B testing to enhance accuracy and user satisfaction.

• Innovate in recommendation engine development, employing collaborative and content-based filtering to curate highly personalized travel suggestions.

• Foster a culture of excellence and continuous learning within the team, mentoring junior members and sharing knowledge to uplift organizational expertise in data science.

What you know

• 5+ years of hands-on experience in data science, machine learning, and predictive analytics, with a proven track record of delivering innovative solutions, preferably in travel or e-commerce industries.

• Proficiency in programming languages such as Python (or R, but we typically use Python here), and experience with data science libraries/frameworks.

• Deep knowledge of machine learning algorithms including supervised learning (e.g., classification, regression), unsupervised learning (e.g., clustering, dimensionality reduction), and semi-supervised learning.

• Thorough understanding of statistical methods, probability theory, and mathematical concepts underlying machine learning algorithms.

• Hands-on experience with deep learning frameworks and familiarity with convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms.

• Ability to design and optimize scalable data processing pipelines for handling large volumes of structured and unstructured data efficiently.

• Demonstrated understanding of software engineering principles and best practices, including version control (e.g., G