- Identify high-impact business opportunities and define data science strategies to address them.
- Translate ambiguous product or business problems into structured, solvable machine learning challenges.
- Design and implement robust ML models, focusing on measurable business outcomes and model
interpretability.
- Drive collaboration with engineering and product teams to ensure successful, scalable deployment of ML
solutions in production.
- Set up monitoring systems for live models, including real-time tracking of data drift, performance degradation,
and failure modes.
- Promote engineering excellence and knowledge-sharing by mentoring junior team members and setting teamwide best practices.
- Present data-driven insights and model results clearly and effectively to influence product strategy and
stakeholder alignment.
What Were Looking For:
- 3+ years of hands-on experience in Data Science and Machine Learning,
including real-world model deployment.
- Strong technical expertise in NLP, Deep Learning, Ranking Algorithms, and Ensemble Methods.
- Proficient in Python with strong engineering discipline and experience in scalable model development.
- Experience with ML deployment frameworks (e.g., Snowpark, MLflow) and production environments.
- Familiarity with modern ML libraries and frameworks such as PyTorch or TensorFlow.
- Skilled in SQL and comfortable working with large-scale structured datasets.
- Academic background in Engineering, Statistics, Computer Science, or related disciplines.
- Solid business acumen, excellent communication skills, and a scientific, data-driven mindset.
- Exposure to cloud platforms like GCP and warehouse like Snowflake is an added advantage.