Data Scientist/Machine Learning Engineer

Role & responsibilities

Job Overview:

Machine Learning Engineer

Key Responsibilities:

• Data Preparation & Analysis

  :
• Gather, clean, and preprocess structured, semi-structured, and unstructured data from various sources.
• Conduct exploratory data analysis (EDA) to identify trends, patterns, and outliers.
• Apply data wrangling techniques using

  Pandas

  ,

  NumPy

  , and

  SQL

  to transform raw data into usable formats.
• Use statistical analysis to drive data-driven decision-making.

• Machine Learning Model Development

  :
• Build, train, and fine-tune machine learning models using

  Scikit-learn

  ,

  TensorFlow

  ,

  Keras

  , or

  PyTorch

  .
• Develop predictive models, classification algorithms, clustering models, and recommendation systems.
• Conduct hyperparameter optimization using techniques like grid search or random search.

• Model Evaluation & Optimization

  :
• Evaluate model performance using metrics such as

  Accuracy

  ,

  Precision

  ,

  Recall

  ,

  F1-Score

  ,

  AUC-ROC

  ,

  Confusion Matrix

  , and

  Cross-validation

  .
• Improve model performance through techniques such as feature engineering, data augmentation, and regularization.
• Deploy models into production environments, and monitor performance for continual improvement.

• Data Visualization & Reporting

  :
• Develop dashboards and reports using

  Tableau

  ,

  Power BI

  ,

  Matplotlib

  ,

  Seaborn

  , or

  Plotly

  .
• Present findings through clear visualizations and actionable insights to non-technical stakeholders.
• Write detailed reports on data analysis and machine learning results, ensuring transparency and reproducibility.

• Collaboration & Stakeholder Communication

  :
• Work closely with cross-functional teams (e.g., engineering, product, business) to define data-driven solutions.
• Communicate technical concepts clearly to non-technical stakeholders and provide insights that influence product and business strategy.

• Data Pipeline & Automation

  :
• Design and implement scalable data pipelines for model training and deployment using

  Airflow

  ,

  Apache Kafka

  , or

  Celery

  .
• Automate data collection, preprocessing, and feature extraction tasks.

• Research & Continuous Learning

  :
• Stay up-to-date with the latest trends in machine learning, deep learning, and data science methodologies.
• Explore new tools, techniques, and frameworks to improve model accuracy and efficiency.

Required Skills:

• Programming Languages

  : Strong proficiency in

  Python

  , with experience in

  SQL

  .

• Machine Learning

  : Hands-on experience with

  Scikit-learn

  ,

  TensorFlow

  ,

  Keras

  ,

  PyTorch

  , or similar ML libraries.

• Data Analysis

  : Strong skills in

  Pandas

  ,

  NumPy

  , and

  Matplotlib

  for data manipulation and analysis.

• Statistical Analysis

  : Experience applying statistical methods to data, including hypothesis testing and regression analysis.

• Cloud Platforms

  : Familiarity with

  AWS

  ,

  Azure

  , or

  Google Cloud

  for deploying models and using cloud-native data services (e.g.,

  AWS Sagemaker

  ,

  Azure ML

  ).

• Data Visualization

  : Experience using

  Tableau

  ,

  Power BI

  ,

  Matplotlib

  ,

  Seaborn

  , or

  Plotly

  for creating visualizations.

• SQL & Databases

  : Proficiency in

  SQL

  for querying relational databases and working with

  NoSQL

  databases (e.g.,

  MongoDB

  ,

  BigQuery

  ).

• Version Control

  : Experience using

  Git

  for version control.

Desirable Skills:

• Big Data Technologies

  : Familiarity with tools like

  Apache Hadoop

  ,

  Spark

  ,

  Dask

  , or

  Google BigQuery

  for processing large datasets.

• Deep Learning

  : Experience with deep learning frameworks such as

  TensorFlow

  ,

  PyTorch

  , or

  MXNet

  .

• NLP & Computer Vision

  : Experience with natural language processing (NLP) using

  spaCy

  ,

  NLTK

  , or

  transformers

  , and computer vision using

  OpenCV

  or

  TensorFlow

  .

• MLOps

  : Familiarity with MLOps tools like

  Kubeflow

  ,

  MLflow

  , or

  DVC

  for managing model workflows.

• Data Engineering

  : Experience with ETL tools like

  Apache Airflow

  ,

  Talend

  ,

  AWS Glue

  , or

  Google Dataflow

  for data pipeline automation.

Tools & Technologies:

• Machine Learning

  :

  Scikit-learn

  ,

  TensorFlow

  ,

  PyTorch

  ,

  Keras

  ,

  XGBoost

  .

• Data Analysis

  :

  Pandas

  ,

  NumPy

  ,

  Matplotlib

  ,

  Seaborn

  ,

  Plotly

  .

• Cloud Platforms

  :

  AWS

  ,

  Google Cloud

  ,

  Azure

  .

• Databases

  :

  MySQL

  ,

  PostgreSQL

  ,

  MongoDB

  ,

  BigQuery

  ,

  Snowflake

  .

• Data Visualization

  :

  Tableau

  ,

  Power BI

  ,

  Matplotlib

  ,

  Seaborn

  ,

  Plotly

  .

• Version Control

  :

  Git

  .