{"id":2728733,"url":"https://alion.io/job/mphasis-senior-data-scientist-3","title":"Senior Data Scientist","company":{"id":59446,"name":"Mphasis","domain":"mphasis.com","url":"https://alion.io/company/mphasis","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":100000,"max_usd":190000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1181},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BigQuery","optional":false},{"name":"Databricks","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Plotly","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Streamlit","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-10-16T17:15:01Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-16T22:11:47Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Senior Data Scientist\nJob Title: Senior Data Scientist - Remote\nLocation: United States (US time zones)\nExperience: 4+ years of applied data science delivery\nAbout the Role\nYou will work as a forward deployed data scientist: embedded with enterprise clients, turning ambiguous business problems into working analytical and AI solutions, and staying with the work until it runs in their environment. This is a hands-on senior role. You will own problems end to end - scoping with stakeholders, wrangling messy client data, choosing methods that fit the constraints, building the solution, and handing it off in a state another team can operate.\nSome engagements need something built from scratch. Many do not - the work is adopting, configuring and extending what already exists: our own products and accelerators, the client’s current platform, or a vendor tool. We are looking for people who are comfortable either way, who can tell which situation they are in, and who would rather extend a working solution than rebuild it.\nWe are looking for modern technical builders rather than notebook-only analysts: fluent with Python, SQL, Git, cloud and cloud data platforms, AI-native in how they work, credible in front of a client, and pragmatic about when a simple method is enough and when deeper modelling is warranted.\nEducation and background: a quantitative degree - statistics, computer science, engineering, operations research, economics, mathematics, physics, or similar. A master’s is preferred. A PhD is welcome but not expected. A strong bachelor’s candidate is equally of interest where there is clear evidence of applied delivery.\nWhat You Will Do\nPartner directly with client stakeholders to translate business problems into analytical problems - and push back when the framing is wrong.Build reliable analytical assets on messy enterprise data: forecasting, optimization, ML, statistical models, and the pipelines around them.\nJust as often, work with what is already there - configure and extend our existing products and accelerators, or the client’s existing tools, rather than rebuilding from scratch.\nPut something in a stakeholder’s hands early - a prototype app, a dashboard, a clear analysis - and iterate from real feedback.\nShip code others can reuse: tested, documented, reproducible, reviewed.\nWork alongside data engineers and ML engineers through to deployment, rather than throwing work over the wall.\nUse LLMs and AI coding tools to move faster, while keeping statistical judgment and final review your own.\nRaise the level of the people around you: review work and mentor less experienced colleagues.\nRequired\nEngineering and tooling\nStrong proficiency in Python and SQL, at a level where your code goes to other people rather than only into your own notebook.Comfortable with Git, pull requests, code review, terminal workflows, package management, and reproducible environments.Working experience on at least one major cloud (AWS, Azure, or GCP) and with a modern data platform such as Snowflake, Databricks, BigQuery, Redshift, Spark, or equivalent.Able to turn exploratory analysis into reusable, tested, and documented code.Comfortable working inside an existing codebase or product as well as starting from a blank page - reading someone else’s code, understanding how it works, and extending it without breaking it.Able to independently troubleshoot common data, query, environment, and pipeline issues - failed jobs, broken queries, bad joins, package conflicts, data-quality problems - without immediately requiring an engineer.Data scienceStrong practical data science skills, including working with messy enterprise data and translating business problems into actionable analysis.Working knowledge of forecasting, optimization, machine learning, and statistical methods - with the judgment to know when a simple method is sufficient, and the discipline to validate honestly (proper backtesting and out-of-time validation for time series, not just a random train/test split).Able to answer “did this actually work?” with defensible evidence: experiment design and read-out, or quasi-experimental methods when a clean test is not possible.Regular user of LLMs or AI coding tools for coding, analysis, debugging, testing, and documentation, while maintaining independent judgment. You should be able to explain what AI output you accepted, rejected, rewrote, and tested, and how you spot output that is plausible but wrong.Basic understanding of production data/ML concepts, including pipelines, data quality, reproducibility, versioning, deployment, and monitoring.","description_format":"text","description_chars":4605,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","Custom Software Development"],"lifecycle":[{"event":"open","at":"2026-10-16T17:15:01Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 1,327 · green card filings: 215","filings_12m":1327,"filings_prev_12m":1471,"green_card_filings_12m":215,"median_offered_wage_usd":122500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":68}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":37,"reasons":["conf:1","win:early"],"computed_at":"2026-10-17T00:04:52Z"},"pay":null,"html_url":"https://alion.io/job/mphasis-senior-data-scientist-3","json_url":"https://alion.io/job/mphasis-senior-data-scientist-3.json","meta":{"generated_at":"2026-10-17T00:04:52Z","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":99,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"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":59446},"rest":"https://alion.io/mcp/rest/get_company?id=59446"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fmphasis-senior-data-scientist-3"},{"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%2Fmphasis-senior-data-scientist-3"},{"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%2Fmphasis-senior-data-scientist-3"},{"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/mphasis-senior-data-scientist-3\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fmphasis-senior-data-scientist-3"}]}