Senior Data Scientist
New
J
JobgetherDevice identification
Based in IndiaFull-TimeSenior
Salary152,000 - 205,000 USD per year
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Job Details
- Languages
- Fluent English
- Experience
- 5+ years of professional experience spanning machine learning, data science, and backend development or closely related engineering disciplines.
- Required Skills
- SQLGitMachine LearningCI/CD
Requirements
- Have 5+ years of professional experience spanning machine learning, data science, and backend development or closely related engineering disciplines.
- Bring advanced knowledge of machine learning fundamentals and statistical methodologies.
- Have practical supervised learning experience, including gradient boosting and approaches for high-cardinality categorical data.
- Have hands-on experience with semi-supervised and unsupervised learning techniques.
- Demonstrate exploratory data analysis and problem-solving skills with incomplete, noisy, or unlabeled datasets.
- Have experience developing real-time machine learning services, including model inference and integration with production applications.
- Be able to turn machine learning models into minimum viable real-time web services and production-ready solutions.
- Have strong coding and software engineering skills, including SQL, Git, CI/CD pipelines, IDEs, and shell scripting.
- Be fluent in English and able to collaborate in a distributed international environment.
- A research mindset and academic background are advantageous.
- Experience with Go and backend development is a plus.
- Familiarity with ClickHouse, Snowflake, BigQuery, dbt, Superset, Tableau, Looker, Pinecone, FAISS, Qdrant, or AWS is beneficial.
Responsibilities
- Develop data-driven algorithms using raw, noisy, and unlabeled data to improve browser and device identification.
- Design and implement supervised, semi-supervised, and unsupervised machine learning approaches, including methods for high-cardinality categorical data.
- Own data science initiatives from problem definition and experimentation through production deployment and integration with real-time services.
- Design experiments and technical solutions for real-time inference, model-to-service integration, and training automation.
- Conduct exploratory data analysis to investigate questions, identify anomalies, and evaluate models and datasets.
- Develop approaches for collecting and evaluating data when labeled datasets are limited or unavailable.
- Share tools, methodologies, and data science practices with colleagues.
- Collaborate with data science and engineering teams to turn machine learning concepts into production-ready services.
- Participate in a shared on-call rotation.
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