Description

Job Description

Responsibilities:
 

​
 

  • As a Head of Data Science, work extensively with the Senior Management in evolving the right Go To
     
  • Market strategies, assisting the Sales Team in acquiring new customers, and proactively identifying
     
  • opportunities through account mining
     
  • Use statistical and machine learning techniques to develop predictive models, algorithms, and data
     
  • products.
     
  • Work with complex data sets to extract insights and develop actionable recommendations for business
     
  • stakeholders.
     
  • Design, develop and implement machine learning pipelines and production-ready systems that handle
     
  • high traffic and real-time data.
     
  • Collaborate with cross-functional teams to integrate data science models into business systems and
     
  • applications.
     
  • Create and maintain documentation on data science models, algorithms, and pipelines.
     
  • Keep up-to-date with the latest developments in machine learning, data science, and related fields and
     
  • apply them to business problems as needed.
     
  • Develop natural language processing, text understanding, classification, pattern recognition,
     
  • recommendation, targeting, ranking, or similar systems.
     
  • Troubleshoot and solve issues with data pipelines, machine learning models, and data systems.
     
  • Develop and maintain APIs for model deployment.
     
  • Requirements:
     
  • Must-Have
     
  • Bachelor's or Master's in Computer Science, Data Science, Statistics, or a related field:
     
  • Fundamental for understanding the theoretical aspects of the role.
     
  • Excellent understanding of machine learning algorithms, processes, tools, and platforms: Core to
     
  • the role of a Data Scientist.
     
  • Proven experience as a Data Science Developer, Data Scientist, or similar role: Validates
     
  • practical skills and experience.
     
  • Strong knowledge of Python programming and building APIs: Python is often the language of
     
  • choice for data science, and API skills are essential for deployment.
     
  • Proficient in SQL and experience with big data platforms like Hadoop, Spark, or similar
     
  • technologies: Necessary for data manipulation and analysis.
     
  • Strong mathematical skills (e.g., statistics, algebra): Fundamental for data modeling and analysis.
     
  • Expertise in LLMs - OpenAI, LLAMA 2 etc
     
  • Good-to-Have
     
  • Experience with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-
     
  • learn): Enhances the ability to implement advanced models.
     
  • Experience with cloud services (GCP, Azure, AWS): Important for scalable data storage and
     
  • computation.
     
  • Strong experience in data wrangling, data cleaning, data preprocessing, and data visualization
     
  • using tools such as Pandas, NumPy, and Matplotlib: Enhances the quality of data analysis.
     
  • Experience with distributed data/computing tools: Useful for handling large datasets.
     

  •  
  • Nice-to-Have
     
  • Knowledge of database systems such as MySQL, MongoDB, and PostgreSQL and familiarity
     
  • with SQL and NoSQL databases: Add versatility but are not critical for the role.
     
  • Experience with natural language processing (NLP) techniques such as sentiment analysis, text
     
  • classification, and topic modeling, and proficiency in using NLP libraries such as NLTK, spaCy,
     
  • and Gensim: Specialized skills that are beneficial for specific projects but not universally
     
  • required.

Key Skills
Education

Any Graduate

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