Data Scientist (Offshore)

Overview

Leads as well as develops scalable AI solutions using relevant AI (ML/DL/Gen AI) techniques

Architects large scale AI solutions that seamlessly merge AI model and techniques in SDLC

Organizes and leads comprehensive code and design review sessions, driving discussions to align with project requirements and best practices. Mentor and provide feedback to junior and mid-level team members.

Conducts research and stays up to date with the latest advancements in AI and machine learning technologies, frameworks, and algorithms. Explore and experiment with cutting-edge techniques to solve complex problems and improve existing models.

Collaborates with cross-functional teams to understand business requirements and design AI and machine learning solutions. Determine the appropriate algorithms, models, and frameworks to use and architect the overall system to ensure scalability, efficiency, and robustness.

Develops, implements, and optimizes machine learning models and algorithms. This includes data pre-processing, feature engineering, model selection, hyperparameter tuning, and training on large datasets. Continuously monitor and improve model performance and accuracy.

Leverage or build analytics tools that utilize the data pipeline to provide significant insights into customer case data, bug data, operational and other key business performance metrics.

Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources

Work with data and analytics specialists to strive for greater functionality in our data systems.

Identify trends, patterns from dataset to scope opportunities for automation

Deploys machine learning models into production environments, considering scalability, performance, and security considerations.

Integrate models with existing software systems and infrastructure, ensuring smooth operation and interoperability.

Monitors the performance of deployed models, collects relevant metrics, and analyzes data to identify areas for improvement. Based on insights gained from monitoring and analysis, fine-tune models, optimize algorithms, and enhance system performance.

Works collaboratively with the engineering manager and team lead to set design and implementation standards, ensuring continuous improvement and alignment with project goals.

Regularly leads meetings, fostering a collaborative and productive team environment.

Has experience in providing technical leadership, mentorship, and guidance to junior team members.

Address and resolve challenges proactively.

Develops and delivers strategic presentations and reports to senior stakeholders, demonstrating a deep understanding of technical and business aspects. Provide insights and recommendations.

Applies and leverages data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets.

Qualifications and Desired Experience:
We are looking for a candidate with 9+ years of experience in a Data science role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field.

5+ years experiences building data pipelines for data science-driven solutions and deployed in Production environment

Experience working in technical support environment, working with dataset from CRM, H/W and S/W bugs data, machine logs

Experience supporting and working with multi-functional teams in a multidimensional fast paced environment.

Good team worker with excellent interpersonal, written, verbal and presentation skills

Experience building and optimizing data pipelines, architectures and data sets.

Experience performing root cause analysis on internal and external data and processes to answer specific business questions and find opportunities for improvement.

Strong analytic skills related to working with unstructured datasets.

Build processes supporting data transformation, data structures, metadata, dependency and workload management.

A successful history of manipulating, processing and extracting value from large, disconnected datasets.

Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.

Strong hands-on coding skills (preferably in Python) processing large-scale data set and developing machine learning model

A strong foundation in mathematics and statistics. In-depth knowledge of linear algebra, calculus, probability theory, and statistical concepts. Understanding and developing complex machine learning models and algorithms.

Good knowledge on Software Development Life Cycle and Agile principles

Experience working with Large Language models, Generative AI, Conversational AI

Familiar with one or more machine learning or statistical modeling tools such as Numpy, ScikitLearn, MLlib, Tensorflow, NLP libraries

Experience working with Databricks, Snowflake platforms

Experience with AWS, S3, Spark, Kafka, Elastic Search

Experience with big data tools: Hadoop, Spark, Kafka, etc.

Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.

Experience with data pipeline and workflow management tools

Experience with AWS cloud services: EC2, EMR, RDS, Redshift

Experience with stream-processing systems: Storm, Spark-Streaming, etc.