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Click here or scroll down to respond to this candidateCandidate's Name
Email : EMAIL AVAILABLE
Linkedin : LINKEDIN LINK AVAILABLE
Phone : PHONE NUMBER AVAILABLE
Address : New York City, NY, United States
SUMMARY
ML engineer with 10 years of hands-on experience crafting advanced machine learning solutions.
Proficient in a myriad of ML techniques, including supervised, unsupervised, and reinforcement
learning, honed through ten years of practical application. Skilled in implementing cutting-edge
deep learning architectures using TensorFlow, PyTorch, and Keras, with a proven track record of
delivering high-accuracy models across diverse domains. Expertise extends to data preprocessing,
feature engineering, and model optimization, ensuring robust performance and scalability.
Proficient in Python, R, and Julia, with a focus on developing clean, efficient code. Well-versed in
big data technologies like Apache Spark, enabling seamless processing of massive datasets.
Possesses a profound understanding of statistical principles and domain-specific knowledge,
facilitating the translation of business needs into actionable ML solutions. Exceptional
communicator and collaborator, adept at driving project success through effective cross-functional
teamwork. Eager to leverage a decade of experience and passion for ML to tackle new challenges
and drive innovation.
SKILLS
Expertise in Deep Learning:
Proficiency in deep learning frameworks such as TensorFlow, PyTorch, and Keras, with experience in
designing, implementing, and optimizing deep neural networks for tasks such asimage recognition, natural
language processing, and time-series analysis.
Advanced Machine Learning Algorithms:
Deep understanding and practical experience with a wide range of machine learning algorithms
including supervised and unsupervised learning, reinforcement learning, deeplearning, and ensemble
methods.
Data Preprocessing and Feature Engineering:
Strong skills in data cleaning, preprocessing, and feature engineering techniques to extract meaningful
features from raw data and prepare it for modeling.
Model Evaluation and Optimization:
Ability to evaluate model performance using appropriate metrics, fine-tune hyperparameters, and
optimize models for better accuracy, speed, and scalability.
Software Development:
Proficiency in programming languages like Python, R, or Julia for building scalable and efficient ML
systems, and experience with version control systems like Git and software development best practices
such as modularization and documentation.
Big Data Technologies:
Familiarity with big data technologies such as Apache Hadoop, Spark (including Databricks), or Dask for
handling large-scale datasets and distributed computing.
Deployment and Productionization:
Experience in deploying machine learning models into production environments, containerization using
Docker, and orchestration using tools like Kubernetes. Knowledge ofcloud platforms like AWS, Azure, or
Google Cloud for scalable deployment.
Domain Knowledge:
Deep understanding of the domain(s) in which they work, whether it's healthcare, finance, e-commerce,
or any other industry, enabling them to tailor ML solutions to specific business needs and challenges.
Continuous Learning and Adaptability:
ML is a rapidly evolving field, so a willingness to stay updated with the latest research, techniques, and
tools, as well as adaptability to new challenges and technologies, are crucial skills for a seasoned ML
developer.
Data Engineering and Warehousing:
Expertise in designing and maintaining data pipelines, ETL processes, and data warehousingsolutions using
tools like AWS Glue, Redshift, and Snowflake.
Cloud Computing and Integration:
Proficient in integrating cloud services and managing data workflows across multiple platforms,
including AWS, Azure, and Google Cloud. Experience with cloud-based data warehousing and
analytics using Snowflake and Databricks.
Generative AI (GenAI):
Experience in leveraging Generative AI models to enhance data processing, analysis, and predictions,
integrating advanced AI capabilities into business solutions for improved insightsand performance.
WORK EXPERIENCE
Senior Machine Learning Engineer - Ibex Jan-2021 - present
At Ibex, I led initiatives to enhance operational efficiency and customer experience in the airline industry
using advanced machine learning techniques. I developed predictive maintenance models using time
series analysis, optimizing aircraft maintenance schedules and minimizing downtime. Additionally, I
spearheaded the deployment of computer vision solutions for quality control in aircraft components, ensuring
adherence to safety standards. I leveraged Databricks for scalable data processing and utilized GenAI
models to improve sentiment analysis from customer feedback, refining service strategies. This involved
integrating Databricks with Snowflake and using its machine learning capabilities to process large datasets
efficiently, contributing to operational excellence and a seamless customer journey.
Mid-Level Data Scientist - Clustox 2017-07 - 2020-11
At Clustox, I applied advanced machine learning techniques to optimize targeted advertisingstrategies
within advertising agencies. I developed sentiment analysis models using natural language processing
(NLP) for social media data, enhancing brand perception understanding.I implemented recommendation
systems using collaborative filtering algorithms, resulting in a 15% increase in click-through rates. My role
also included designing customer segmentationmodels for personalized campaigns. Utilizing Databricks, I
managed large-scale data processing workflows and employed GenAI to enhance ad performance
prediction models. This integration allowed for real-time data analysis and significantly improved
advertising effectiveness by leveraging Databricks' distributed computing capabilities in conjunction with
Snowflake's robust data warehousing solutions.
Data Strategy Analyst - Urban Planning 2013-10 - 2017-05
I played a pivotal role in shaping data-driven decisionmaking processes Leveraging my technical skills in
data analytics and machine learning, I developed and implemented predictive models for credit risk
assessment, optimizing loan approval processes My proficiency in SQL and data manipulation tools
facilitated the extraction and transformation of large datasets, providing valuable insights for strategic
decision-making Additionally, I applied time series analysis techniques to forecast market trends, aiding
the bank in adaptingto dynamic economic conditions My responsibilities extended to developing and
maintaining data governance frameworks, ensuring data quality and compliance with regulatory
standards Utilizing statistical analysis tools, I conducted thorough assessments of financial data, enabling
the bank to make informed investment decisions Furthermore, I implemented machine learning
algorithms for fraud detection, contributing to a significant reduction in fraudulent activities My technical
acumen, coupled with a deep understanding of financial data, played a crucial role in enhancing the
bank's data strategy and fostering data-driven innovation in the banking sector.
EDUCATION
Bachelor of Science: Computer Sciences
COMSATS UNIVERSITY 2009 - 2013
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