{"id":4225701,"url":"https://alion.io/job/factset-software-engineer-iii-python-data-engineer-2","title":"Software Engineer III - Python Data Engineer","company":{"id":34841,"name":"FactSet","domain":"factset.com","url":"https://alion.io/company/factset","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":{"grade":"B","score":83,"open_postings":3,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":22,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"Java","optional":false},{"name":"Keras","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Amazon EC2","optional":true},{"name":"Amazon Kinesis","optional":true},{"name":"Amazon Redshift","optional":true},{"name":"Amazon S3","optional":true},{"name":"ArangoDB","optional":true},{"name":"AWS","optional":true},{"name":"AWS Glue","optional":true},{"name":"Azure","optional":true},{"name":"Cassandra","optional":true},{"name":"Computer Vision","optional":true},{"name":"DynamoDB","optional":true},{"name":"GCP","optional":true},{"name":"Git","optional":true},{"name":"Hadoop","optional":true},{"name":"HBase","optional":true},{"name":"NLP","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-11-13T07:35:10Z","employer_posted_date":null,"last_verified_at":"2026-11-13T07:35:10Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"At FactSet, our values are the foundation of everything we do. They express how we act and operate, serve as a compass in our decision-making, and play a big role in how we treat each other, our clients, and our communities. We believe that the best ideas can come from anyone, anywhere, at any time, and that curiosity is the key to anticipating our clients' needs and exceeding their expectations.\n\nYour Team's Impact:\n\nJoin our dynamic Machine Learning and AI team, where we build innovative models and solutions that drive business transformation and unlock new opportunities. You'll be at the forefront of AI-powered initiatives, collaborating closely with product teams to shape the future of data-driven insights. This position offers high visibility and the chance to directly influence key decisions across the organization. You will report to the Manager of AI.\n\nWorking Mode: Hybrid (3 days mandatory office)\n\nWhat You'll Do:\nIngest and analyze various data sources to drive innovation in content creation.\nAutomate the acquisition, relevance scoring, and storage of incoming sources.\nDevelop processes for data mining, data concordance, and data production.\nExplore and evaluate new data technologies to build a scalable, cloud-oriented data platform.\nOptimize data retrieval, develop dashboards, and other visualizations for financial experts.\n\nSystem & Pipeline Development:\nArchitect and implement scalable ML pipelines for training, validation, inference, and monitoring.\nCollaborate with data engineers to ensure high-quality data ingestion, feature engineering, and labeling workflows.\nContribute to MLOps practices by building reproducible, testable, and maintainable model delivery frameworks.\nAssist in designing, developing, and implementing machine learning models for real-world applications.\nWork on data collection, preprocessing, feature engineering, and model evaluation tasks.\nCollaborate with cross-functional teams including Data Science, Software Engineering, and Product.\nPerform exploratory data analysis (EDA) and prepare datasets for training/testing.\nContribute to the deployment and monitoring of models in production environments.\nWrite clean, efficient, and well-documented code in Python or similar languages.\nStay updated with the latest developments in AI/ML research and tools.\nAssist in model optimization, hyperparameter tuning, and performance scaling.\nStay current with the latest industry trends and technologies, contributing innovative ideas to ongoing projects.\nTest and validate models to ensure their reliability and effectiveness in production environments.\nWork with large datasets to extract meaningful insights using various statistical and ML techniques.\n\nWhat We're Looking For:\nEducational Background: Bachelor's or master's degree in computer science, engineering, or a related field is required.\nExperience: 3-5 years of experience in software development, with a focus on systems handling large-scale data operations.\nStrong foundation in Machine Learning; AI and Agentic Development concepts (supervised/unsupervised learning, regression, classification, clustering, etc.).\nGood programming skills in Python (or similar languages like R, Java, C++).\nHands-on experience with ML libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch, Keras).\nUnderstanding of data structures, algorithms, and basic mathematics/statistics and database management systems.\nExcellent verbal and written communication skills, capable of articulating complex concepts to technical and non-technical audiences.\nFamiliarity with data handling tools (e.g., Pandas, NumPy, SQL).\nGood analytical, problem-solving, and communication skills.\nAbility to learn new technologies quickly and work independently or as part of a team.\nAbility to work collaboratively in a team environment, contributing to group success while expanding personal skills.\n\nDesired Skills:\nStrong experience and proficiency with Python, Pandas, NumPy, and AWS APIs.\nExperience with modern data platforms such as Spark, Hadoop, or other map/reduce big data systems and services.\nExperience with a variety of data stores such as ArangoDB, MongoDB, Cassandra, HBase, DynamoDB.\nExperience with AWS environment e.g., Amazon Kinesis, AWS Glue, Redshift, EC2, S3, EMR.\nExposure to deep learning, NLP, computer vision, or reinforcement learning projects (academic or internships).\nKnowledge of cloud platforms like AWS, Azure, or GCP.\nFamiliarity with version control systems (e.g., Git).\nUnderstanding of MLOps concepts and pipelines (bonus).\nSkills\nAI, AWS, Amazon Web Services, Artificial Intelligence, ML, Machine Learning, 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