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Selected role

Sr ML Engineer and Data Scientist

Recruiterthon LLC

Senior Full-time

*Position: ML Data Scientist Position

Location: Remote

Duration: 6-12 months of contract with possible extension

Role Summary:**

Ideal candidate will have strong
**Time-Series forecasting, sales forecasting, LGBM (LightGBM) and Darts library.

Qualifications:**

Master’s plus degree in Computer Science, Statistics, Applied Mathematics, or a related field.
7+ years of experience in data science and machine learning, with a proven track record of delivering models to production.
Proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
Strong understanding of statistical modeling, machine learning algorithms, and experiment design.
Solid experience with SQL and data manipulation tools (e.g., Pandas, Spark, or Dask).
Experience deploying models using APIs (Flask, FastAPI), Docker, and orchestration tools (e.g., Airflow, Kubeflow, MLflow).
Hands-on experience with cloud platforms (AWS, GCP, or Azure) and model serving tools.
Excellent problem-solving and communication skills; able to explain complex concepts clearly and effectively.

Preferred:

Experience with time series forecasting, causal inference, recommendation systems, or NLP.
Familiarity with data versioning and reproducibility tools (e.g., DVC, Weights & Biases).
Exposure to feature stores, streaming data (e.g., Kafka), or real-time ML systems.
Background in MLOps and experience building generalizable ML frameworks or platforms.

Core Technical Skills:

ML Engineer Preferred: Ideally, the candidate should be an ML Engineer, though seasoned Data Scientists with relevant experience are suitable.
Python & SQL: Strong coding and data manipulation skills.
Time-Series Forecasting:Experience with LGBM (LightGBM) and Darts library.
MLOps Expertise Preferred: Hands-on experience with Astronomer, Airflow, and DAG creation.
Capable of building wrappers and scalable pipelines. This skill is highly valuable, but not a deal breaker.
Cloud Platforms: Proficient in AWS, with exposure to GCP preferred.
Debugging & Troubleshooting: Skilled in investigating and resolving issues in Python experiments and executions.
GitHub Proficiency: Comfortable working in repositories with many contributors, managing branches, pull requests, and code reviews.
Collaboration & Work Style
Self-Starter: Able to work independently and proactively contribute ideas.
Team-Oriented: Willing to support Roman and Calvin while offering directional guidance on model enhancements.
Fast Learner: Quick to adapt to new tools, workflows, and business contexts to rapidly onboard into the project.

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