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ML/DL Engineer

Remote:

Full Remote

Contract:

Full time

Experience:

Mid-level (2-5 years)

Work from:

Poland

Offer summary

Qualifications:

3+ years of experience in data science and machine learning, Expertise in time-series forecasting, Strong coding skills in Python and SQL, Experience with TensorFlow/PyTorch and Pandas, Good understanding of ML systems in production.

Key responsabilities:

Develop and implement machine learning models for demand forecasting
Conduct research to improve model performance
Collaborate with cross-functional teams to maintain ML systems
Mentor junior team members and promote best practices
Communicate technical insights to non-technical stakeholders

Fetcherr Scaleup https://fetcherr.io/

11 - 50 Employees

See more Fetcherr offers

Job description

Senior Data scientist / Machine / Deep Learning Engineer

Fetcherr experts in deep learning, algo-trading, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions.

We are seeking a talented senior machine learning engineer / data scientist to help us advance our machine learning capabilities. The ideal candidate should be a self-driven, motivated, and independent thinker who is passionate about using data and machine learning to drive business outcomes.

Responsibilities:

Develop and implement cutting-edge machine learning models and algorithms for demand forecasting applications.
Conduct research and experimentation to identify and evaluate new approaches for improving model accuracy and performance.
Collaborate with cross-functional teams, including business stakeholders, data engineers, and software developers, to deploy and maintain machine learning systems in production.
Mentor and train junior team members, promoting best practices and fostering a culture of continuous learning and improvement.
Communicate technical findings and insights to non-technical stakeholders, including executives and other decision-makers.

Requirements:

Must have:

3+ years of hands-on experience in data science and machine learning.
Expertise in time-series forecasting, with experience working in demand forecasting or related contexts.
Strong coding skills in Python and SQL, with experience using related open-source libraries and frameworks, including TensorFlow/PyTorch and Pandas.
Experience building tabular machine learning models using gradient boosting methods and deep learning
Strong understanding of machine learning systems in production, including good coding practices for testing, reproducibility, and version control.
Excellent written and verbal communication skills.

Nice to have:

Degree in Computer Science, Statistics, or related quantitative field.
Published papers, patents, or professional posts.
Experience in leveraging deep learning and machine learning in domains such as finance/trading, reinforcement learning, or natural language processing.
Experience with MLOps and cloud platforms like GCP.
Experience with workflow orchestration tools like Apache Airflow or Dagster to schedule and monitor machine learning workflows.
Strong data visualization and data analysis skills.
Knowledge of code optimization, cloud computing, containerization, and continuous integration/continuous deployment (CI/CD) pipelines.
Competitive programming and data science (Kaggle like) exp