Job Description
HCLTech is hiring Machine Learning Engineer.
Job Title:
Machine Learning Engineer / 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 .