- LocationBangalore, Bengaluru / Bangalore, India
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IndustryFinance
Responsibilities
● Design, develop, and deploy machine learning models and algorithms for various
applications such as classification, regression and clustering. Utilize deep learning
techniques and frameworks to solve complex problems.
● Evaluate and fine-tune machine learning models to achieve optimal performance
metrics. Conduct experiments and A/B testing to improve model performance and
explore different algorithmic approaches.
● Analyze complex and large-scale datasets using statistical and machine learning
techniques. Develop and implement predictive models, algorithms, and statistical
analyses to extract insights and drive decision-making processes.
● Work closely with cross-functional teams, including data engineers, software developers,
and business stakeholders, to understand requirements and develop
data-driven solutions. Collaborate with team members to tackle complex problems and
provide guidance to junior data scientists.
● Stay up-to-date with the latest research and advancements in NLP, ML, and DL, and
apply innovative techniques to address business challenges effectively.
Qualifications
● A bachelor's or master's degree in a quantitative field such as Computer Science, Data
Science, Statistics, Mathematics, or a related discipline.
● 4 to 6 years of hands-on experience in data science, machine learning, and statistical
analysis. Experience in applying data science techniques to real-world business
problems, preferably in the financial or fintech industry.
● Proficiency in programming languages such as Python, with a strong understanding of
data manipulation, statistical analysis, and machine learning libraries and frameworks.
● Proven track record of designing and implementing NLP models and algorithms for
various tasks, along with experience in handling unstructured textual data.
● Experience in working with big data technologies and cloud platforms preferably AWS.
● Familiarity with popular data science tools and frameworks such as scikit-learn, pandas,
or Spark. Proficiency in SQL for data retrieval and manipulation from databases.
● Experience in deep learning techniques and frameworks such as TensorFlow, PyTorch,
or Keras. Experience in developing and deploying deep learning models. Ability to
leverage pre-trained models and transfer learning to optimize model performance in
specific use cases.
● Good written and verbal communication skills, with the ability to explain complex data
science concepts to both technical and non-technical audiences.
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