Algorithm Manager

Role Overview

We are seeking a visionary and hands-on Algorithm Manager to lead our data science and algorithm engineering team. In this role, you will bridge the gap between complex mathematical research and scalable product implementation. You will be responsible for defining the technical roadmap, mentoring a team of high-performing engineers, and ensuring our algorithms drive significant business impact.

Key Responsibilities

l Team Leadership: Lead, recruit, and mentor a team of algorithm engineers and data scientists. Foster a culture of technical excellence and continuous learning.

l Technical Strategy: Define the long-term vision for algorithm development, including machine learning (ML), optimization, or signal processing (depending on the specific domain).

l Project Management: Oversee the end-to-end lifecycle of algorithm development-from research and prototyping to production deployment and monitoring.

l Cross-functional Collaboration: Work closely with Product, Hardware, and Software Engineering teams to integrate algorithms into user-facing products.

l Innovation: Stay at the forefront of AI/ML research, identifying new technologies and methodologies that can give the company a competitive edge.

l Code Quality & Standards: Establish best practices for code reviews, testing, and documentation within the algorithm team.

Required Qualifications

l Education: Master's or Ph.D. in Computer Science, Mathematics, Statistics, or a related quantitative field.

l Experience: 7+ years of experience in algorithm development or machine learning.

l 3+ years of experience in a leadership or people management role.

l Technical Proficiency: Expertise in Python, C++, or Java

l Deep understanding of ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).

l Strong foundation in linear algebra, probability, and optimization theory.

l System Design: Proven track record of deploying large-scale algorithmic systems in a production environment.

Preferred Skills (The Plus Factors)

l Experience with Big Data technologies (Spark, Flink) or Cloud platforms (AWS, GCP, Azure).

l A strong publication record in top-tier conferences (ICML, NeurIPS, CVPR, etc.).

l Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.