Responsibilities:

<ul>
<li>Strategic Collaboration: Collaborate closely with product and business teams to gain a comprehensive understanding of the challenges and opportunities within the GenAI landscape, aligning data science initiatives with the organisation’s objectives.</li>
</ul>

<ul>
<li>Metric Definition: Define key performance metrics that accurately reflect the value delivered to end-users through GenAI solutions.</li>
</ul>

<ul>
<li>Intelligent System Design: Apply deep expertise in machine learning, statistics, and advanced mathematics to conceptualize, experiment, and design intelligent systems powered by GenAI.</li>
</ul>

<ul>
<li>Big Data Processing: Develop efficient systems capable of processing vast volumes of data, demonstrating proficiency in distributed programming frameworks like Hadoop and Spark.</li>
</ul>

<ul>
<li>ML/AI Architecture: Collaborate closely with ML Engineers to design scalable systems and model architectures that enable real-time ML/AI services.</li>
</ul>

<ul>
<li>End-to-End ML Pipelines: Take ownership of ML pipelines from start to finish, encompassing data pre-processing, model generation, cross-validation, and feedback sharing.</li>
</ul>

Qualifications:

<ul>
<li>A Bachelor’s degree or higher in Computer Science, Statistics, Mathematics, or a related field, with a minimum of 10 years of relevant work experience.</li>
</ul>

<ul>
<li>Strong problem-solving and programming skills, coupled with a profound understanding of the mathematical foundations underpinning machine learning algorithms, including probability, statistics, linear algebra, calculus, and optimization.</li>
</ul>

<ul>
<li>Proven track record of successfully deploying ML projects in production systems with substantial individual contributions.</li>
</ul>


<ul>
<li>Experience with natural language processing tasks utilizing prompt engineering, LLMs, transformers, and knowledge graphs is highly advantageous.</li>
</ul>

<ul>
<li>Proficiency in time series analysis, NLP, Distributed Systems, large-scale computing, and Big Data technologies such as Spark is a plus.</li>
</ul>

<ul>
<li>A solid background in at least 3 of the following areas: Natural language processing, statistical ML techniques, graph algorithms, constraint optimization, signal processing (speech or vision), deep learning, and distributed systems.</li>
</ul>

<ul>
<li>Proficiency with database systems and schema design, encompassing both SQL and NoSQL databases. </li>
</ul>

Data Scientist

Responsibilities: Strategic Collaboration: Collaborate closely with product and business teams to gain a comprehensive understanding of the challenges and opportunities within the GenAI landscape, aligning data science initiatives with the organisation’s objectives. Metric Definition: Define key performance metrics that accurately reflect the value delivered to end-users through GenAI solutions. Intelligent System Design: Apply deep expertise in machine learning, statistics, and advanced mathematics to conceptualize, experiment, and design intelligent systems powered by GenAI. Big Data Processing: Develop efficient systems capable of processing vast volumes of data, demonstrating proficiency in distributed programming frameworks like Hadoop and Spark. ML/AI Architecture: Collaborate closely with ML Engineers to design scalable systems and model architectures that enable real-time ML/AI services. End-to-End ML Pipelines: Take ownership of ML pipelines from start to finish, encompassing data pre-processing, model generation, cross-validation, and feedback sharing. Qualifications: A Bachelor’s degree or higher in Computer Science, Statistics, Mathematics, or a related field, with a minimum of 10 years of relevant work experience. Strong problem-solving and programming skills, coupled with a profound understanding of the mathematical foundations underpinning machine learning algorithms, including probability, statistics, linear algebra, calculus, and optimization. Proven track record of successfully deploying ML projects in production systems with substantial individual contributions. Experience with natural language processing tasks utilizing prompt engineering, LLMs, transformers, and knowledge graphs is highly advantageous. Proficiency in time series analysis, NLP, Distributed Systems, large-scale computing, and Big Data technologies such as Spark is a plus. A solid background in at least 3 of the following areas: Natural language processing, statistical ML techniques, graph algorithms, constraint optimization, signal processing (speech or vision), deep learning, and distributed systems. Proficiency with database systems and schema design, encompassing both SQL and NoSQL databases.

Data Scientist in Chennai (UID-11471)

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