Senior Applied Data Scientist (AI/ML)

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Senior Applied Data Scientist (AI/ML)

We are looking for an experienced Data Scientist to build, evaluate, and productionize machine learning models that drive measurable business impact. You will lead end-to-end model development—from problem framing and data preparation to experimentation, deployment, and monitoring—while raising the bar on rigor, reproducibility, and velocity.

Key Responsibilities:

• End-to-End Model Development : Translate business objectives into ML problems; scope datasets; build, iterate, and validate models (supervised/unsupervised, time series, deep learning); take models from notebook to production.
• Feature Engineering & Data Pipelines : Design reliable datasets and features; implement robust ETL/ELT in SQL/Python (and ideally Spark); partner with data engineering on feature stores, data quality checks, and lineage.
• Experimentation & Evaluation : Define metrics and baselines; run structured experiments (CV, holdouts, A/B tests); perform error analysis, bias/fairness checks, and model explainability; document results and tradeoffs.
• Model Deployment & Monitoring : Collaborate with engineering to package and ship models (batch, streaming, real-time); instrument monitoring for performance, drift, and business KPIs; establish retraining and rollback strategies.

Core Skills & Experience Required:

• Machine Learning & Statistics : Strong grasp of classic ML (regression, trees/GBMs, clustering), feature selection, model validation, and statistical testing.
• Deep Learning : Hands-on experience with PyTorch or TensorFlow; practical understanding of modern architectures. LLM exposure (prompting, fine-tuning/LoRA, embeddings/RAG) is a plus.
• Data Wrangling & SQL : Proficiency in Python (pandas, NumPy) and SQL; experience working with large datasets. Spark or similar distributed frameworks is a plus.
• Experiment Tracking & Reproducibility : Comfortable with MLflow/Weights & Biases/DVC; versioning of code, data, and models; reproducible environments (conda/poetry).
• Model Serving & APIs : Experience packaging models and exposing inference via services (e.g., FastAPI/Flask) and integrating with upstream/downstream systems.
• Programming & Version Control : Strong Python fundamentals, testing, and Git workflows; ability to write clean, production-ready code.

Preferred Qualifications:

• Demonstrated impact shipping models to production with measurable business outcomes and post-deployment monitoring.
• Experience collaborating with product/engineering to define problem statements, success metrics, and experiment designs; excellent communication of tradeoffs to non-technical stakeholders.
• Ability to operate autonomously in a remote setting, manage multiple workstreams, and communicate clearly across time zones.

FAQs:

What’s the next step? If successful, you’ll have one more interview before receiving a job offer if you're a good fit.

What happens after submission? We’ll review your application within 2–3 business days and contact you if you qualify for the next stage.

When would I start? As soon as possible, with flexibility to accommodate your circumstances.

How long does it take? About 5-10 minutes to complete.