1 day ago
Hyderābād, IndiaSenior
Responsibilities
- Ingest, analyze, acquire, score, store, and produce data from varied sources.
- Develop data mining, data concordance, data production, and scalable cloud-oriented data platform processes.
- Architect and implement ML pipelines for training, validation, inference, monitoring, and model delivery.
- Support data ingestion, labeling, preprocessing, feature engineering, exploratory data analysis, and dataset preparation.
- Develop, evaluate, optimize, tune, test, validate, deploy, and monitor machine learning models in production.
- Build reproducible, testable, and maintainable MLOps frameworks and contribute to model delivery practices.
- Develop dashboards and visualizations and extract insights from large datasets using statistical and machine learning techniques.
- Write clean, efficient, and documented Python or similar-language code and collaborate with data, software, product, and research teams.
Requirements
- Bachelor’s or master’s degree in computer science, engineering, or a related field.
- 3–5 years of software development experience focused on systems handling large-scale data operations.
- Strong foundation in supervised and unsupervised learning, regression, classification, clustering, data structures, algorithms, mathematics, statistics, and database management.
- Strong programming skills in Python or similar languages such as R, Java, or C++.
- Hands-on experience with scikit-learn, TensorFlow, PyTorch, or Keras.
- Familiarity with Pandas, NumPy, SQL, data handling, and database tools.
- Strong experience with Python, Pandas, NumPy, and AWS APIs is desired.
- Experience with Spark, Hadoop, or other map/reduce big data systems and services is desired.
- Experience with ArangoDB, MongoDB, Cassandra, HBase, or DynamoDB is desired.
- Experience with AWS services such as Amazon Kinesis, AWS Glue, Redshift, EC2, S3, and EMR is desired.
- Exposure to deep learning, NLP, computer vision, or reinforcement learning projects is desired.
- Knowledge of AWS, Azure, or GCP; familiarity with Git; and understanding of MLOps concepts and pipelines are desired.
- Strong analytical, problem-solving, communication, collaboration, independent-working, and learning skills.
Benefits
- Hybrid work arrangement with three mandatory office days.
- Health, life, and disability insurance, retirement savings plans, discounted employee stock purchase program, paid holidays, family leave, and company-wide wellness days.
- Flexible work accommodations supporting work/life harmony.
- Volunteerism and sustainability opportunities and company Business Resource Groups.
- Career progression planning with dedicated monthly learning and development time.
Tech Stack
Amazon DynamoDBAmazon RedshiftApache CassandraApache HadoopApache HBaseApache SparkAWSAzureC++GitGoogle Cloud PlatformJavaKerasMongoDBNumPyPandasPythonPyTorchRscikit-learnSQLTensorFlow
Categories
Data EngineeringML Engineering
About FactSet
FactSet builds a financial data and analytics platform used by asset managers, investment banks, hedge funds, and wealth managers for research, portfolio management, trading, and risk. Its products include the FactSet workstation, data feeds and APIs, and workflow tools like OMS/EMS, sold primarily via subscription licensing. Founded in 1978 and headquartered in Norwalk, Connecticut, FactSet is a public company listed on the NYSE (ticker: FDS).