Machine Learning Specialist

HCLTech is hiring Machine Learning Specialist.

Job Title:

Machine Learning Specialist / Data Scientist / Data Analyst

Job Overview:

We are seeking a skilled
Machine Learning Engineer
,
Data Scientist
, or
Data Analyst
to design, develop, and deploy machine learning models, conduct deep data analysis, and generate actionable insights. The ideal candidate will have experience in data preprocessing, feature engineering, model development, and performance optimization, working with large datasets and leveraging advanced machine learning frameworks.

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:

5+yrs of experience in below mentioned 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
  .