About the role

Tech Lead Data Scientist at Aera Technology driving enterprise-level ML solutions with customer focus. Leading data science projects, mentoring junior staff, and integrating AI in customer environments.

Responsibilities

Act as the senior technical point of contact, driving successful outcomes by identifying, scoping, and leading data science projects that directly address high-value customer problems.
Measure and report on the business impact (ROI, efficiency gains) of deployed models.
Ensure rigorous testing and validation of all new data science features and model deployments.
Design, implement, monitor, and maintain scalable, production-grade Data Science systems.
Research and evaluate the latest in Machine Learning, Generative AI, and advanced statistical models for new feature development.
Actively scout, evaluate, and implement the latest advancements in Data Science, Machine Learning (MLOps, LLMs/Generative AI), and AI to maintain Aera’s technical edge.
Maintain a zero-tolerance policy for delayed customer resolution related to Data Science models and pipelines, working closely with Support, Customer Success and Product teams to resolve issues and implement robust, long-term solutions.
Directly lead and mentor a small, high-impact team (2-3 members) including Data Scientists, Senior Data Scientists, and Interns, setting technical standards, guiding project execution, and fostering individual career growth.
Translate customer feedback and cutting-edge research into actionable product features and technical specifications for the engineering and product teams.

Master’s or Bachelor’s Degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field with a focus on Machine Learning/Deep Learning/AI.
7+ years of progressive professional experience as a Data Scientist or Machine Learning Engineer, ideally in a B2B SaaS or product development setting.
Proven, hands-on experience designing, developing, and deploying systems utilizing the latest advancements in AI (e.g., MLOps practices, Generative AI, vector databases, or transformer models).
Strong experience with the full ML lifecycle, including translating research models into scalable, production-ready services using modern software development practices.
Exceptional proficiency in SQL and Python; able to write high-quality, high-performance, and maintainable code.
Experience with distributed systems and ML frameworks such as Dask, Ray, or Spark.
Familiarity with production ML services like Kubeflow, Sagemaker, or similar cloud-native MLOps tools is a significant advantage.
Demonstrated experience in leading projects, setting technical direction, and mentoring or supervising junior data professionals. Experience leading teams or performing hiring is a strong plus.