Machine Learning Data Scientist
il y a 3 heures
Adikteev is a leading company in the app ecosystem, inspiring users to engage with their favorite apps. With over 100 employees and offices in Paris, San Francisco, and Seoul, Adikteev operates globally.
Salary
We offer a competitive salary of $120,000 per year, along with a performance-based quarterly bonus, profit-sharing scheme, longevity bonus, and healthcare plan.
Job Description
As a Machine Learning Engineer at Adikteev, you will design, deploy, monitor, maintain, and improve machine learning and deep learning models in production. You will work closely with the Engineering team to optimize model performances while considering inference time and data volume constraints.
You will also perform custom analyses to provide insights to various stakeholders, investigate performance shifts, A/B test, evaluate business potential, and presale analysis. Additionally, you will mentor a junior Machine Learning Engineer.
Required Skills and Qualifications
To be successful in this role, you should have a Master's degree or PhD in STEM, preferably with a statistics focus. You should have 5+ years of experience working in teams using data science to power a core company product or service. Applied experience with machine learning on large datasets is essential.
You should have proficiency in querying, structuring, and processing qualitative or quantitative data to draw insightful business conclusions. Knowledge of various machine learning techniques and their real-world advantages/drawbacks is required. Technical skills include programming in Python (main packages: pandas, numpy, scipy, scikit-learn, tensorflow...), coding best practices, basic knowledge of database languages (SQL), competency in shell scripting, experience with deployment to Production, and experience with AWS a plus (S3, EC2, EMR, Glue, SageMaker...).
Benefits
We offer a range of benefits, including flexible remote working policy, parental leave policy, mental health support, €9 lunch vouchers (50% covered by Adikteev), and a hands-on use case to be completed remotely, followed by a restitution with Gwenaëlle, Senior Data Scientist, and Thomas.
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