Senior Data Scientist

Posted 2024-09-16

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💎 Seniority level: Senior, 8+ years

📍 Location: USA, UK

💸 Salary: $175K - $250K

🔍 Industry: Influencer Marketing

🗣️ Languages: English

⏳ Experience: 8+ years

🪄 Skills: AWSPythonSQLData AnalysisHadoopMachine LearningPyTorchAlgorithmsData analysisData engineeringData scienceSparkTensorflowCommunication SkillsCollaboration

Requirements:

8+ years experience working with large datasets and datastores, conducting large-scale quantitative analyses.
8+ years of experience in machine learning engineering, with a proven track record of developing and deploying successful ML projects.
Deep understanding and practical experience in building and optimizing ‘people data’ machine learning models (especially knowledge graphs).
Conceptual familiarity with social media data and ad tech concepts.
Proficiency in data analysis using tools such as Python, R, or similar languages.
Expertise working with large datasets, datastores (especially AWS), and data processing technologies (Hadoop, Spark, Pig/Hive).
Strong experience with AWS services (including SageMaker) for model training, deployment, and management.
Expert SQL scripting required.
Preferred: Proficiency in TensorFlow, PyTorch, or similar ML frameworks and libraries.
Strong communication skills, with the ability to engage effectively with both technical and non-technical stakeholders.
Degree (preferred Master's or Ph.D.) in a relevant field such as Data Science, Machine Learning, Statistics, Computer Science, or a related discipline.

Responsibilities:

Lead the development, optimization, and productionalization of foundational machine learning projects and algorithms.
Work directly with dataOps and core platform teams to understand modeling opportunities across existing datasets, and bring technical depth to concept ideation.
Work with large datastores to stage and process large volumes of influencer marketing-related data, designing, building, and supporting new pipelines of data transformation, conversion, and validation.
Interface with enterprise clients to understand problems that the design, development, and deployment of ML-backed proof of concepts can address.
Communicate findings and results to both technical and non-technical stakeholders through clear and concise reports and presentations.
Contribute to the development and maintenance of data science best practices, standards, and tools within the organization.

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