Machine Learning / Analytical Ability

Machine Learning Engineer / Data Scientist

Key Responsibilities

• Machine Learning Model Development:

• Design, develop, and implement

  machine learning models

  from conception to deployment.
• Perform

  feature engineering, model selection, training, tuning, and validation

  .
• Utilize various ML algorithms (e.g., supervised, unsupervised, reinforcement learning) to solve complex business problems.

• Data Analysis & Preprocessing:

• Conduct in-depth

  exploratory data analysis (EDA)

  to understand data characteristics, identify trends, and discover insights.
• Perform

  data cleaning, transformation, and preprocessing

  to prepare data for model training.
• Work with large and diverse datasets, ensuring data quality and integrity.

• Problem Solving & Analytical Thinking:

• Translate complex business challenges into quantifiable problems that can be solved using data and machine learning.
• Apply strong

  analytical and critical thinking skills

  to interpret model results, validate assumptions, and draw actionable conclusions.
• Propose innovative data-driven solutions and articulate their potential impact to stakeholders.

• Deployment & MLOps:

• Assist in the deployment of ML models into production environments, ensuring scalability, reliability, and performance.
• Monitor model performance post-deployment and implement strategies for continuous improvement and retraining.
• Familiarity with

  MLOps principles

  for efficient lifecycle management of ML models.

• Collaboration & Communication:

• Collaborate closely with cross-functional teams including product managers, software engineers, and business analysts.
• Clearly communicate complex analytical findings and technical concepts to both technical and non-technical audiences.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
• Proven experience in developing and deploying

  machine learning models

  .
• Strong proficiency in

  Python

  or

  R

  for data analysis and machine learning.
• Expertise with relevant ML libraries/frameworks (e.g.,

  Scikit-learn, TensorFlow, Keras, PyTorch

  ).
• Solid understanding of

  statistical modeling, hypothesis testing

  , and

  experimental design

  .
• Proficiency in

  SQL

  for data extraction and manipulation.
• Demonstrable

  analytical and problem-solving skills

  with a keen eye for detail.
• Excellent

  communication and presentation skills

  .

Preferred Skills

• Experience with

  cloud platforms

  (e.g., AWS SageMaker, Azure ML, Google Cloud AI Platform).
• Familiarity with

  big data technologies

  (e.g., Spark, Hadoop).
• Knowledge of

  data visualization tools

  (e.g., Tableau, Power BI).
• Experience with

  version control systems

  (e.g., Git).