Data Scientist

Overview

A Data Scientist with 4+ years of experience has moved beyond basic model building into the realm of end-to-end ownership and strategic influence. At this level, you are expected to not only produce accurate insights but also ensure those insights are scalable, reproducible, and aligned with business goals.

Core Responsibilities
1. Data Strategy & Preparation
At the mid-to-senior level, the focus shifts from "cleaning data" to building robust data pipelines.

Feature Engineering: Designing complex features that capture nuanced business logic.

Data Quality Governance: Implementing automated checks to detect data drift or corruption before it hits the model.

Advanced Wrangling: Handling massive, unstructured datasets using distributed computing frameworks like Spark or Dask.

2. Model Development & Validation
This is the heart of the role, requiring a deep understanding of the "why" behind the "how."

Experimental Design: Selecting the right architecture (e.g., Gradient Boosting, Transformers, or Reinforcement Learning) based on the specific constraints of the problem.

Hyperparameter Optimization: Using systematic approaches like Bayesian optimization to fine-tune performance.

Rigorous Testing: Moving beyond simple accuracy to look at precision-recall trade-offs, F1-scores, and A/B testing frameworks to validate real-world impact.

3. Deployment & Collaboration
You will act as the bridge between "the lab" and "the product."

MLOps Collaboration: Working with ML Engineers to containerize models (using Docker/Kubernetes) and integrate them into CI/CD pipelines.

Model Monitoring: Establishing dashboards to track model performance in production and setting up "retraining triggers."

Optimisation: Partnering with engineers to reduce inference latency and optimize memory usage for real-time applications.

Required Technical Skills

1. Programming: Advanced Python/R, SQL (expert level), and Spark.
2. Modeling: Deep Learning (PyTorch/TensorFlow), Tree-based models (XGBoost/LightGBM).
3. Cloud & DevOps: AWS/GCP/Azure ML suites, Docker, Git, and MLflow.
4. Mathematics: Linear Algebra, Calculus, and Advanced Statistics (Bayesian Inference).