{"id":1271055,"url":"https://alion.io/job/careerfairy-data-analytics-intern-h1-2027","title":"Data Analytics Intern (H1 2027)","company":{"id":2231107,"name":"CareerFairy","domain":"careerfairy.io","url":"https://alion.io/company/careerfairy","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16000,"max_usd":33000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":8},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Databricks","optional":true},{"name":"NumPy","optional":true},{"name":"Power BI","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Tableau","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-09-25T00:00:00Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-25T08:44:49Z","board_verified":true,"closed_at":null,"days_open":31,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":31},"description":"As a Data Analytics Intern - Trading, you'll work on real business requirements alongside analysts, quants, and data engineers. Your job is to help us get more from our data - extracting and cleaning it, automating manual work, and building the tools and analytics that trading teams rely on.\nThis is a hands-on, technical role. You'll be expected to code - from data cleaning in Python or SQL through to automation, analysis, and the use of AI. We'll match the depth of your work to your skills and to what the desk needs.\nThis role sits within the analytics function supporting the trading desks, turning data into insights that inform market decisions - with exceptional candidates having the opportunity to work increasingly closely with the desks over time.\nAdvanced scope (quant & engineering): If you have serious quantitative and coding depth, you can go further - building statistical/ML models and backtesting research on large datasets that feed directly into trading workflows.\nKEY RESPONSIBILITES\nExtract and clean data from APIs, web sources, portals, and internal databases\nWrite Python/SQL to build the pipelines, scrapers, and tools the desk needs\nApply LLMs and AI tools to problems that used to take manual effort\nPrepare charts, dashboards, and summaries for the desk\nTrack vessel movements, cargo flows, refinery events, and supply/demand indicators\nInvestigate and flag data-quality issues\n Advanced Scope (Quant & Engineering)Develop statistical and machine-learning models for trading workflows\nRun quantitative research and backtesting on large datasets\nBuild production-grade Python and automated data pipelines\nOptimise and refactor existing systems for performance and reliability\n\nEDUCATION AND EXPERIENCE REQUIREMENT\nPursuing a degree in a STEM, quantitative, analytical, or business field - for example Engineering, Computer Science, Data Science, Mathematics, Statistics, Quantitative Finance, Economics, Finance, Business, or Business Analytics\nAn ability to code in Python and/or SQL - from data cleaning upward - or clear evidence that you can learn fast\nStrong analytical, problem-solving, and attention-to-detail skills\nThe ability to take a business requirement and deliver a working result\nAn interest in commodities, shipping, or data-driven analytics\nNICE TO HAVE\nExperience with data analysis, web scraping, or light automation\nHands-on with Python data libraries (e.g. pandas, NumPy)\nFamiliarity with statistics or machine learning (e.g. scikit-learn, PyTorch, TensorFlow)\nExposure to cloud/data platforms (Databricks, AWS, Azure)\nBI tools (Power BI, Tableau)\nUnderstanding of commodity fundamentals or shipping logistics\nWho we are\nGlencore is one of the world’s largest global diversified natural resource companies and a major producer and marketer of more than 60 commodities. Through a network of assets, customers and suppliers that spans the globe, we produce, process, recycle, source, market and distribute the commodities that advance everyday life.\nWith over 140,000 employees and contractors and a strong footprint in over 30 countries in both established and emerging regions for natural resources, our marketing and industrial activities are supported by a global network of offices.\nGlencore’s customers are principally industrial consumers, such as those in the automotive, steel, power generation, battery manufacturing and oil sectors. We also provide financing, logistics and other services to producers and consumers of commodities.\nImportant information\nThis material does not purport to contain all of the information you may wish to consider. For further important information, including in connection with forward-looking statements and other cautionary information, refer to the Important notice section of Glencore’s 2025 Annual Report, which is available at glencore.com/publications. By their nature, forward-looking statements involve known and unknown risks, uncertainties and other factors which may cause actual results, performance or achievements to differ materially from any future events, results, performance, achievements or other outcomes expressed or implied by such forward-looking statements. This document does not constitute or form part of any offer or invitation to sell or issue, or any solicitation of any offer to purchase or subscribe for any securities.\nOther information\nThe companies in which Glencore plc directly and indirectly has an interest are separate and distinct legal entities. In this document, “Glencore”, “Glencore group” and “Group” are used for convenience only where references are made to Glencore plc and its subsidiaries in general. These collective expressions are used for ease of reference only and do not imply any other relationship between the companies. Likewise, the words “we”, “us” and “our” are also used to refer collectively to members of the Group or to those who work for them. These expressions are also used where no useful purpose is served by identifying the particular company or companies.\nNote*: All applications will be subject to potential background checks. You will be informed if this will be the case. Further information about the processing of your personal data will be made available during the application process. We'll seek your explicit consent prior to processing the data.","description_format":"text","description_chars":5326,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-26T00:10:20Z"}],"visa":[],"liveness":{"score":58,"band":"ok","label":"Likely open","p_open":1,"p_active":0.776,"p_room":0.75,"age_days":31,"expected_fill_days":33,"reasons":["conf:21","velocity","win:late","comp:junior"],"computed_at":"2026-10-26T05:57:25Z"},"pay":null,"html_url":"https://alion.io/job/careerfairy-data-analytics-intern-h1-2027","json_url":"https://alion.io/job/careerfairy-data-analytics-intern-h1-2027.json","meta":{"generated_at":"2026-10-26T13:08:32Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":18250,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-27T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2231107},"rest":"https://alion.io/mcp/rest/get_company?id=2231107"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fcareerfairy-data-analytics-intern-h1-2027"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fcareerfairy-data-analytics-intern-h1-2027"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fcareerfairy-data-analytics-intern-h1-2027"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/careerfairy-data-analytics-intern-h1-2027\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fcareerfairy-data-analytics-intern-h1-2027"}]}