Data Science Manager

**Detalles de la oferta**:
Yalo
Hi
This is Yalo
We are on a mission to bring conversational commerce to the world...
Remember how it used to be to interact with businesses that knew and understood you, that could recommend exactly what you needed, and that with a simple message could get you what you wanted???
Yep... neither do we.
That is why at Yalo we are marrying the scale of digital commerce with the personalization and simplicity of conversations to help companies delight their users.
We know that traditional SAAS companies focus on first-world problems... we don't
Having started in Latin America, our roots are in Emerging Markets and therefore we care about bringing amazing experiences to a population that traditionally has been underserved, such as the small shop owner in Brazil who is ordering online for the first time.
If you're looking for a place to make things happen, learn fast, and impact emerging markets in a way that hasn't been done before, look no further.
Come Join us in our mission of improving billions of lives through the power of conversational commerce
Your missionWe are looking for a Data Science Manager to lead our applied data science efforts in building and scaling an intelligence platform that powers recommendations, personalization, and emerging Generative AI/NLP use cases.
This role is not just about model delivery — it's about translating data science into business impact, empowering a team of high-caliber data scientists, and aligning closely with product and operations stakeholders to prioritize what matters most.
Outcomes (6-12 months)Deliver High-Impact Use Cases: Lead the team in delivering at least two data science use cases (e.g., recommendation engine, demand forecasting, segmentation) that improve core business KPIs such as conversion, retention, or revenue growth.

Maximize Senior Talent: Staff-level and senior data scientists are operating at full potential with clear priorities, ownership, and support — resulting in accelerated delivery and higher impact.

Stakeholder Trust & Roadmap Alignment: A shared data science roadmap is co-developed with Product, Operations, and Engineering.
Business stakeholders are aligned and actively participating in prioritization.

Model to Production Flow: At least one key model or algorithmic solution is delivered to production through a pragmatic model lifecycle process — including tracking, iteration, and clear ownership.

Cross-Team Execution Unlocked: The manager is consistently identifying and resolving cross-functional dependencies, ensuring progress is not blocked and that initiatives ship on time.

Impact Visibility: Business teams and leadership have clear visibility into the value delivered by data science — through updates, dashboards, storytelling, or internal demos.

ResponsibilitiesLead a High-Caliber Team: Manage, mentor, and empower a team of applied data scientists, with a focus on business ownership and delivery excellence.

Drive Roadmap and Prioritization: Own and continuously refine the data science roadmap in collaboration with product and operations stakeholders.
Ensure work aligns with the most urgent and valuable opportunities.

Enable Impact from Staff ICs: Ensure senior individual contributors are unblocked, strategically guided, and operating effectively in delivering high-leverage work.

Stakeholder Partnership: Act as a trusted partner to business units, translating their challenges into solvable data problems and aligning initiatives with their OKRs.

Narrative and Communication: Champion data storytelling, writing internal memos or updates that clarify impact, rationale, and next steps — enabling faster, more informed decision-making.

Own Cross-Team Execution: Manage project dependencies, unblock issues across teams, and ensure timely delivery of cross-functional initiatives.

Model Lifecycle Guidance: Promote lean model development and iteration processes — focusing on launching solutions that work, not just those that are technically sophisticated.

Job Requirements (Must Have)Experience: 7-10 years in data science or analytics, with 2+ years managing high-seniority ICs (e.g., staff or principal-level contributors).

Business-Oriented Thinking: Strong ability to frame work in terms of business impact and customer value.
Comfortable deprioritizing technically interesting work that lacks ROI.

Execution and Prioritization: Proven record of managing competing priorities and driving focused execution in a dynamic environment.

Stakeholder Alignment: Strong communicator with experience working closely with product and ops teams.
Able to lead through influence and build alignment across functions.

Team Leadership: Skilled at setting direction, coaching senior talent, and building a high-trust, high-performance team culture.

Communication: Comfortable presenting to non-technical stakeholders and senior leadership.
Can articulate trade-offs, results, and rationale in plain language.

Desirable (Nice