Lead Machine Learning Scientist

As a Lead Machine Learning Scientist at Manychat, you will be spearheading the development of generative AI products, collaborating closely with product teams, and overseeing the entire ML lifecycle from development to deployment. Your role also includes establishing testing protocols for AI outputs and providing technical leadership to your team. Expect to leverage your expertise in machine learning to drive business value and innovation.

Candidates for the Lead Machine Learning Scientist role at Manychat should possess at least six years of experience in machine learning roles, with a strong background in developing and deploying ML models. Experience with generative AI and familiarity with tools such as TensorFlow, PyTorch, and SQL is essential, along with leadership skills and effective cross-functional communication capabilities.

Manychat is known for its vibrant and inclusive work culture that celebrates diversity. As a Lead Machine Learning Scientist, you will be part of a collaborative environment where innovation thrives. We prioritize professional development, offer flexible work arrangements, and value employee well-being with various health and wellness benefits.

For the Lead Machine Learning Scientist position at Manychat, applicants should have strong technical skills in Python, SQL, and ML frameworks like TensorFlow and PyTorch. Experience with MLOps and cloud infrastructures, particularly AWS and Snowflake, will be highly beneficial in enhancing your contributions to the team.

Manychat provides substantial relocation support for candidates moving to Barcelona. This includes assistance with airfare, accommodations for up to three weeks, and a relocation bonus, ensuring that you transition smoothly to your new role in our vibrant city.

Absolutely! In my previous role, I led a project that focused on developing generative AI models for real-time customer interaction. This involved designing the architecture, optimizing the models for performance, and collaborating with engineers to ensure seamless integration with product features.

My approach to building and deploying machine learning models begins with thorough data analysis and feature engineering. I follow an iterative development process where I constantly test and validate the models using real-world data, ensuring they meet business objectives before deploying them into production.

To maintain model accuracy, I implement continuous monitoring strategies that involve setting performance benchmarks and using automated tools to flag anomalies. Regular retraining of the models with updated data is another crucial strategy I employ to ensure they perform optimally.

Mentoring junior team members involves a mix of hands-on guidance and empowering them to make decisions. I encourage them to engage in discussions, share their ideas, and collaborate on projects while providing advice and support when they encounter challenges.

One challenging problem I encountered was dealing with data imbalance in a classification task. I tackled it by implementing tailored sampling techniques and algorithmic adjustments, which significantly improved the model's predictive performance. This experience underscored the importance of adaptive problem-solving in ML.

My experience has involved working closely with product managers and engineers to align ML objectives with product requirements. I prioritize clear communication and regular touchpoints to ensure our ML initiatives are meeting both technical specifications and user needs.

I have extensive experience implementing MLOps practices, including continuous integration and continuous delivery (CI/CD) for ML workflows. This has streamlined our deployment processes and improved our model management throughout their lifecycle, increasing overall efficiency.

I prefer using frameworks like TensorFlow and PyTorch for building ML models due to their flexibility and ease of use. For data manipulation, I often utilize pandas and NumPy. I also leverage cloud services like AWS for model deployment and scaling.

I stay updated by following relevant academic journals, attending conferences, and participating in online workshops. Engaging with other professionals in the AI community through forums and social media also allows me to keep up with cutting-edge developments.

I believe that generative AI will revolutionize marketing by enabling more personalized and scalable customer interactions. This technology enhances user engagement and can greatly improve the efficiency of marketing campaigns, leading to higher conversion rates and customer satisfaction.