Data Scientist (Machine Learning)

Key Responsibilities:

Data Analysis & Exploration:
Gather, clean, and process large datasets from various sources to extract meaningful insights. Perform exploratory data analysis (EDA) to identify trends, patterns, and outliers in data.

Model Development:
Design, build, and evaluate machine learning models (supervised and unsupervised) for classification, regression, clustering, or other tasks using frameworks like TensorFlow, Scikit-Learn, PyTorch, or Keras.

Algorithm Optimization:
Fine-tune model parameters, optimize hyperparameters, and experiment with different machine learning techniques to improve model performance.

Feature Engineering:
Create and select appropriate features from raw data that can enhance model performance and accuracy.

Deploy and Monitor Models:
Deploy models into production and monitor their performance, ensuring scalability and reliability of ML systems. Implement A/B testing to measure the impact of ML solutions.

Collaboration:
Work closely with data engineers, software developers, and business stakeholders to integrate ML models into production systems and drive data-driven decision-making.

Documentation and Reporting:
Maintain detailed documentation of all processes, models, and code. Communicate results and insights clearly to technical and non-technical stakeholders.

Stay Updated:
Keep up with the latest advancements in machine learning, AI, and data science methodologies to apply innovative solutions.

Required Skills & Qualifications:

Technical Skills:

Proficient in Python, R, or Java for data analysis and model development.

Hands-on experience with machine learning frameworks such as TensorFlow, Keras, PyTorch, or Scikit-Learn.

Strong knowledge of statistical modeling, probability theory, and mathematical optimization.

Experience with SQL and NoSQL databases for querying and handling large datasets.

Familiarity with big data tools like Hadoop, Spark, or Kafka is a plus.

Knowledge of cloud platforms such as AWS, Google Cloud, or Azure for deploying models.

Machine Learning Techniques:

Proficient in supervised and unsupervised learning, including classification, regression, clustering, dimensionality reduction, and time-series analysis.

Experience with deep learning, neural networks, and advanced NLP techniques is a plus.

Familiarity with reinforcement learning and graph algorithms is beneficial.

Soft Skills:

Strong problem-solving skills with an analytical mindset.

Excellent verbal and written communication skills for clear reporting of results.

Ability to work both independently and in a team environment.

Strong attention to detail and ability to manage multiple projects.