Data Scientist (DA)

Data Scientist

Fetcherr experts in deep learning, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions.

As our company continues its rapid growth, we're looking for a highly experienced and impactful Data Scientist to join our team. This role is ideal for someone who not only possesses deep technical expertise but also demonstrates the potential to lead and shape our data science initiatives as we scale. You will be instrumental in translating complex data into strategic business insights, driving data-driven decision-making, and mentoring team members.

Requirements:

You’ll be a great fit if…

• You have 4+ years of hands-on data analytics and data science experience, with a proven track record of designing, developing, and deploying data models that directly impact business outcomes and drive significant value.
• You are expert-level proficient in Python, encompassing the full data science and machine learning ecosystem (e.g., pandas, numpy, scikit-learn, statsmodels, PyTorch/TensorFlow for deeper ML applications). You can architect robust and scalable data solutions.
• You possess exceptional communication, presentation, and storytelling skills, capable of translating highly complex technical findings into clear, concise, and actionable narratives for executive leadership, product teams, and non-technical stakeholders. You can influence strategic decisions through data.
• You have extensive experience in proactively identifying complex business problems, translating them into well-defined analytical frameworks, and leading the end-to-end execution of data-driven solutions. You are adept at presenting findings, strategic recommendations, and potential risks to senior leadership.
• You consistently identify, recommend, and lead the implementation of innovative analytical approaches and solutions to complex, ambiguous business challenges. You challenge the status quo and drive continuous improvement in our data science practices.
• You are a natural leader and mentor, ready to guide and empower junior data scientists, foster a collaborative team environment, and drive accountability towards aggressive product and business timelines. You take ownership of team success.
• You excel at structuring highly ambiguous business problems into clear, prioritized, and actionable research questions and sequential execution plans, often involving cross-functional collaboration.
• You have a deep understanding of how to evaluate and optimize the performance of ML-powered systems from both a technical and critical business impact perspective. You can define relevant metrics, build monitoring solutions, and drive iterative improvements based on business goals.
• Degree in a quantitative field such as Economy, Statistics, Mathematics, Computer Science,Data and Decision Sciences or a related discipline.
• You are highly proficient in SQL (with extensive experience in BigQuery / Dataform / Cloud environments being a significant advantage) and demonstrate mastery of git for collaborative development and version control.
• You demonstrate a strong commitment to continuous learning and staying abreast of the latest advancements in data science, machine learning, and relevant technologies. You are keen to introduce new methodologies and tools to the team.
• You have strong English communication skills, both written and verbal, and feel comfortable working in an environment where English is the primary language for collaboration, documentation, and presentations.
  
  

• Deep domain knowledge and established expertise in the travel industry (e.g., airlines, transportation, hospitality), understanding its unique data challenges and opportunities.
• Extensive practical experience with advanced analytical techniques such as causal inference (e.g., A/B testing design and analysis, difference-in-differences), uplift modeling, advanced anomaly detection, econometric modeling, or sophisticated pricing algorithms.
• Proven experience with mathematical optimization techniques and their application to real-world business problems.
• Experience building and deploying production-grade machine learning models (MLOps).