Senior Data Scientist

• Work with stakeholders to understand their needs and translate them into technical requirements: This includes understanding the business goals of the stakeholders, and identifying the data that is needed to achieve those goals. 

• Develop and implement data science solutions: This includes using statistical and machine learning techniques to extract insights from data and develop models that can be used to predict future outcomes or prescribe actions. 

• Evaluate the performance of data science solutions: This includes tracking the accuracy of predictions and the effectiveness of recommendations. 

• Perform advanced data analysis and develop sophisticated predictive and prescriptive models: This includes using statistical and machine learning techniques to extract insights from data and develop models that can be used to predict future outcomes or prescribe actions. 

• Explore and preprocess data to ensure data quality and suitability for analysis: This includes cleaning, formatting, and transforming data to make it ready for analysis. 

• Develop and deploy machine learning models into production environments: This includes making sure that machine learning models are working properly and that they can be used to make predictions or recommendations in real time. 

• Mentor and guide data scientists and promote knowledge sharing: This includes sharing knowledge and expertise with other data scientists, and helping them develop their skills and knowledge. 

• Deliver measurable business value through data-driven insights and recommendations: This includes using data science to identify opportunities to improve the business, and developing and implementing solutions that can help the business achieve its goals. 

• Conducting data analysis 

• Developing data visualizations 

• Integrating data with other applications 

• Troubleshooting data problems 

• Stay abreast of the latest data science advancements and industry trends 

• Writing data documentation 

• Promote the use of data science within the organization 

Requirements

• Bachelor's degree in computer science, statistics, mathematics or a related field (preferred)
• Minimum 3+ years of experience in the relevant field.
• Proven experience in ETL/ELT, Oracle Data Integrator (ODI) is preferred.
• Strong analytical thinking and problem-solving capabilities
• Effective communication and interpersonal skills
• Strong SQL queries, data modelling concepts are mandatory.
• Data mining through state-of-the-art methods.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Experience working with ML/DL frameworks like Scikit-Learn, TensorFlow, Keras, PyTorch, etc. as well as a firm grip over modern state-of-the-art and good-to-have NLP architectures such as BERT, GPT3/GPT4, Reformer, RNNs, CNNs, LSTMs, etc. and vector space models such as GloVe, Word2vec, USE, etc.
• Good to have Familiarity with Model Serving APIs like, Django, Flask & StreamLit. Peer-reviewed publications in topics of advanced data science, machine learning, deep learning, or NLP.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, proper usage, etc.) and experience with applications.
• Expert knowledge and Experience in working on conventional Computational Linguistics problems such as Syntactic Processing, Word Sense Disambiguation, Context-Free grammar, Lexical Semantics, Quantification, Plurality, etc.
• Expert knowledge and Experience in working on conventional NLP problems like Informational Retrieval, Relevance Ranking, and Search, Question-Answer Generation, Natural Language Inference, Topic Modeling, Unstructured Text Pipelines/Modeling, Anomaly Detection, Text Summarization, etc.
• Experience using statistical computer languages (Python, SQL, etc.) to manipulate data and draw insights from large data sets.
• A drive to learn and master new technologies and techniques. Certifications in the relevant domain and demonstratable experience in research and development are a plus.
• Good to have peer-reviewed publications on topics of advanced data science, machine learning, deep learning, or NLP

Benefits

• Competitive salary
• Fuel Card
• Health benefits
• Professional development opportunities
• Inclusive work culture & much more