Overview:
Our Technology teams challenge the status quo and reimagine capabilities across industries. Whether through research and development, technology innovation or solution engineering, our team members play a vital role in connecting consumers with the products and platforms of tomorrow.
Responsibilities:
Candidates must be willing to participate in at least one in-person interview, which may include a live whiteboarding or technical assessment session.
Key Responsibilities:
* Partner with stakeholders to identify high-impact, feasible opportunities and demonstrate how data science can drive business outcomes.
* Develop machine learning models for customer segmentation, churn prediction, user modeling, and lifetime value estimation.
* Design and deploy GenAI and LLM solutions to automate complex workflows, such as transcript analysis and compliance validation.
* Transform and analyze large-scale structured and unstructured data (e.g., text, images) to uncover actionable insights.
* Collaborate with cross-functional teams to build and deploy scalable, cloud-based data science and GenAI solutions; provide technical mentorship.
* Communicate complex models and findings to both technical and non-technical audiences via dashboards, notebooks, and presentations.
Qualifications:
Education and Experience:
* Bachelor's or Master's degree in Statistics, Machine Learning, Computer Science, Engineering, Mathematics, Physics, or a related quantitative field
* 4+ years of experience in data science and applied machine learning, with a proven track record of delivering business impact from data-driven solutions
Skills and Qualifications:
* Strong foundation in statistical and machine learning techniques, including regression, classification, clustering, neural networks, and ensemble models.
* Proficient in Python and SQL for data science workflows; skilled in building and evaluating generative AI and LLM-based solutions.
* Experienced in prompt engineering, model evaluation, and deploying LLMs in production environments.
* Skilled in processing unstructured data (text, images) using modern ML/DL frameworks.
* Proven ability to design, run, and analyze A/B tests and controlled experiments.
* Hands-on experience with AWS cloud environments and enterprise-scale ML platforms like Dataiku and Databricks.
Preferred Qualifications:
* Master's or PhD in a relevant quantitative domain
* Experience with big data tools such as Amazon Athena, Redshift, BigQuery, Spark, or Teradata
* Familiarity with MLOps practices for deploying, monitoring, and maintaining ML/GenAI systems in production
* Data engineering skills for preprocessing, cleaning, and transforming large-scale datasets
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